Black Friday and Cyber Monday are major retail events in Canada, with 43% and 29% of the population making purchases during these sales respectively, according to a YouGov report. Consumer electronics continue to lead the Canadian retail market during these events, with 55% of surveyed shoppers choosing to buy tech products on Black Friday. Household appliances come in second, with 25% of shoppers opting for these items, while 18% prefer to shop for furniture deals.
These statistics highlight the importance of delivering value during the Thanksgiving sales week. Retailers must cater to shoppers’ expectations with competitive pricing, attractive deals, and a seamless shopping experience. So, what unique offerings did Canadian retailers present to shoppers this season?
To understand the pricing and discount dynamics during BFCM 2024 in Canada, DataWeave analyzed discounts across leading consumer electronics and home & furniture retailers. Using our AI-powered pricing intelligence platform, we analyzed 37,108 SKUs across these categories for major retailers including Amazon, Walmart, Best Buy, Home Depot, and Canadian Tire from the 10th to 29th November. We focused on the top 500 products ranked for each search keyword on each retail site, using targeted terms aligned with categories like “sofa” and “wearables”.
In the following insights, the Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.
Consumer electronics saw robust participation from major retailers, with Amazon, Best Buy, and Walmart leading the charge. Here’s how they stacked up in terms of discounts:
Best Buy emerged as the frontrunner in absolute discounts at 31.2%, while Amazon impressed with a notable 19.7% additional discount, indicating a strong Black Friday-specific markdown strategy.
Walmart offered steady competition, particularly in audio and video products, which reached an average absolute discount of 37.2%. However, it’s average additional discount was only 3.1%, indicating muted BFCM-specific price reductions in this category.
Subcategory Insights
Diving deeper into consumer electronics subcategories, we observed varied discounting strategies.
Audio & Video stood out as the most discounted subcategory, with Walmart leading at 37.2%.
In Wearables, Walmart again took the top spot with 36.4%, while Amazon offered higher additional discounts (22.4%).
Discounting for computers and gaming was less aggressive, highlighting strategic pricing to maintain profitability in these high-demand segments.
Brand Performance
Brand-level data highlighted how key players used Black Friday to drive visibility and sales.
Dell led in average absolute discounts (36.7%) followed by Samsung at 36.68%
Audio brand JBL offered significant absolute discounts at 35.9%.
Apple and Lenovo offered comparatively fewer discounts but maintained strong visibility, as seen in their increase in the Share of Search during the sale period.
MSI (laptop brand) and Bose (audio and earphone brand) experienced significant increases in visibility, with Share of Search increases of 5% and 3.6%, respectively.
Notably, HP faced a decline (-3.2%) in the Share of Search, suggesting missed opportunities to align promotions with consumer interest.
Home & Furniture
Retailers in Focus
The home and furniture category saw competitive discounting, with Walmart, Canadian Tire, and Home Depot vying for consumer attention.
Walmart took the lead with the highest absolute discounts at 36.8%. The retailer’s additional discounts were more conservative at 3.6%. This is similar to their discount levels in Consumer Electronics.
Canadian Tire offered stiff competition, providing 31.6% absolute discounts and 25% additional discounts.
Home Depot matched its absolute and additional discounts, maintaining consistency at 24.1%.
Subcategory Insights
Home and furniture subcategories revealed targeted discount strategies.
Bedding emerged as the most discounted subcategory at Walmart (50.6%) and Canadian Tire (35.3%).
Kitchenware saw competitive pricing, with Walmart leading at 42.9%, followed by Canadian Tire at 33.9%.
Canadian Tire focused on lighting, offering the highest absolute discounts in this subcategory (38.2%)
Brand Performance
Brand-level analysis revealed stark contrasts in discounting approaches.
Furniture brands Homcom led in absolute discounts (36.4%), while South Shore stood out with the highest additional discounts (30.2%).
Value-oriented brands like furnishings brand Mainstays and mattress and bedding brand Zinus offered more modest discounts, focusing on consistent affordability.
Zinus (mattresses and sofa brand) experienced a significant 7.9% increase in the Share of Search, driven by aggressive promotions.
Home furnishings brands like Costway and Safavieh faced declines, reflecting the importance of aligning promotional strategies with consumer expectations.
Insights for Retailers and Brands
This Black Friday, Canadian retailers effectively balanced deep discounts with category-specific strategies to maximize sales. However, the fluctuating Share of Search highlights the critical need for brands to align promotions with consumer interest.
For brands and retailers looking to stay ahead of the competition, DataWeave’s pricing intelligence platform offers unparalleled insights to refine discounting strategies and boost visibility. Contact us to learn how we can help you stay competitive in this dynamic retail landscape.
Americans spent a whopping total of $10.8 billion online this Black Friday. As Thanksgiving Week 2024 wraps up, one thing is clear: the consumer electronics category continues to dominate seasonal shopping trends. Fueled by a blend of enticing deals and high consumer demand, the sector delivered competitive discounts across subcategories like wearables, gaming, and mobile devices.
At DataWeave, we analyzed discounting trends in the U.S. consumer electronics market during this year’s sales events. Using our AI-powered pricing intelligence platform, we tracked pricing and promotions for 22383 SKUs across Amazon, Walmart, Target, and Best Buy from November 10 to 29. We focused on the top 500 products ranked for each search keyword on each retail site, using targeted terms aligned with categories like “gaming” and “apple.” Here’s what we uncovered.
Retailers Battle It Out with Competitive Discounts
Discount trends reveal clear leaders in terms of markdowns:
Walmart offered the deepest average absolute discounts at 36.9%.
Amazon and Target followed closely, highlighting a diverse range of deals designed to appeal to budget-conscious shoppers
Best Buy, the specialist consumer electronics retailer, offers the lowest discounts this Black Friday at 26.2%.
Note: The Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.
Subcategory Spotlight: Where the Best Deals Happened
From audio & video to wearables, each retailer carved out competitive advantages across subcategories.
Both Amazon and Walmart offered high discounts in audio & video and wearables, but Walmart led, with discounts up to 46.3%.
Best Buy, meanwhile, offered high absolute discounts on Mobile Devices(34%) and Storage (31%), followed by high discounts on wearables and Audio & Video.
Amazon maintained a balanced approach, excelling in audio & video and mobile devices.
Brand-Level Insights: HP and Samsung Dominate
The biggest winners this year were brands that strategically leveraged Black Friday discounts to boost visibility and sales:
HP took the top spot with average discounts of 36.9%, followed by Samsung at 31.4%.
Despite its premium reputation, Apple offered an average discount of 29.3%, signaling a shift in strategy to attract deal hunters.
Share of Search: Shifting Consumer Attention
Search trends reveal how discounts shaped brand visibility:
Microsoft saw the largest spike in share of search (+8.6%), thanks to aggressive pricing on gaming consoles and accessories.
Marshall and Amazon also saw significant gains in visibility.
Surprisingly, HP experienced a sharp decline (-9.8%), indicating missed opportunities despite steep discounts.
In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 340 matched products across retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.
Here are the key takeaways from this analysis.
Category-Level Highlights
Amazon leads with the highest average discount (41.35%), offering the most value to consumers. It is followed by Target (39.37%) and Walmart (36.15%).
Best Buy, the specialist consumer electronics retailer, ranks last with an average discount of 31.53%, emphasizing a less aggressive pricing strategy compared to competitors.
Subcategory Highlights
Wearables: Amazon offers the steepest discounts (55.40%), followed by Best Buy (50.60%) and Walmart (45.75%).
Mobile Devices: Amazon also leads (37.94%), with Walmart (29.30%) in second place and Target trailing at 19.48%.
Gaming: Target takes the lead (37.47%), with Amazon and Best Buy offering similar discounts around 30%.
Computers: Target again emerges as the leader (39.18%), narrowly surpassing Walmart (36.13%).
Brand Highlights
Apple: Amazon dominates with 53.06%, closely followed by Walmart (50.55%), while Target and Best Buy hover around 43%.
Nintendo: Target edges out Amazon (37.62% vs. 36.54%), with Best Buy (33.21%) and Walmart (25.92%) trailing.
Beats by Dr. Dre: Amazon leads (46.07%), with Target (37.14%) as the runner-up. Best Buy and Walmart offer comparatively modest discounts around 25%.
Bose: Walmart emerges as the value leader (23.90%), surpassing Target (16.09%) and Best Buy (15.29%).
Cricut: Amazon sets a high benchmark (54.13%), with Target far behind (36.43%) for this viral portable printer brand. Best Buy (12.32%) and Walmart (10.79%) offer significantly lower discounts.
What This Means for Retailers and Brands
Retailers looking to stay competitive should focus on strategic discounting and enhanced brand visibility. Brands must align with consumer expectations by:
Leveraging platforms like DataWeave to analyze discount trends.
Optimizing pricing and assortment strategies for seasonal demand.
For more insights into consumer electronics pricing, contact DataWeave to discover how our AI-powered solutions can drive success in today’s fast-paced market. Stay tuned for more category-specific analyses in the coming weeks!
As the holiday shopping season kicked off, savvy shoppers embraced the spirit of the season, drawn by enticing deals. The apparel category is forecasted as the second highest earning category (Source: Statista), expected to generate revenues up to $43.9 billion, closely following consumer electronics. To understand the pricing strategies of top retailers amidst the sale season, DataWeave analyzed the pricing trends for the Apparel category this Black Friday.
We leveraged our AI-powered data platform to analyze the discounting across key retailers. Our analysis focused on the Apparel category, examining how Amazon, Walmart, Target, Saks Fifth Avenue, Nordstrom, Bloomingdales, Neiman Marcus and Macy’s differentiated themselves through their discounts.
For this analysis, we tracked the average discounts of apparel products among leading US retailers during the Thanksgiving weekend sale, including Black Friday. Our sample was chosen to encompass the top 500 ranked products in each product subcategory across during the sale.
Subcategories reported on: Footwear, Kid’s Clothing, Men’s Clothing, Women’s Clothing, Activewear, Plus Size Clothing, Accessories
Timeline of analysis: 10 to 29 November 2024
We focused on the top 500 products ranked for each search keyword on each retail site, using targeted terms aligned with categories like “athleisure” and “plus size clothing”. Our methodology distinguished between standard discounts and Black Friday-specific ‘additional discounts’ or price reductions during the sale compared to the week before, to reveal true consumer value.
Key Findings
This year’s fashion discounts were unprecedented. Let’s take a look.
Retailer Level Insights
Nordstrom leads with the highest average absolute discount at 59%, followed by Saks Fifth Avenue at 35.5% and Bloomingdale’s at 41.5%. Macy’s shows the lowest average discount at 24.1%, while Amazon has an average discount of 30.4%.
Amazon ranks lower in both average absolute and additional discounts compared to competitors, indicating a more conservative discounting strategy.
Subcategory Analysis
Kids’ Clothing saw the deep discounts (up to 55% at Nordstrom), reflecting growing pressure on family budgets and heightened competition to attract budget-conscious parents.
Plus-Size Clothing emerged as a major focus, with Nordstrom leading at 53.22% average absolute discounts, signaling that retailers are increasingly prioritizing size inclusivity and appealing to a broader consumer base.
Footwear experienced robust discounting, particularly at Bloomingdale’s with 37% average absolute discounts, showing a competitive approach to attract customers looking for seasonal footwear deals.
Activewear displayed substantial discounts, with Walmart offering up to 41% on average, aligning with the trend of consumers looking for practical and comfortable attire during the winter season.
Brand Level Insights
Apparel brands, meanwhile, also offer telling insights.
Top Discounting Brands: Aqua leads with an average absolute discount of 44.58%, followed by Boss at 42.33% and Burberry at 37.84%.
Lowest Discounts: Athletic Works shows the lowest average absolute discount at 31.23%, with a minimal additional discount of 3.73%.
Competitive Advantage: Brands like Ralph Lauren and Boss show strong discounts, indicating aggressive marketing during the sale.
Share of Search Insights
Top Gainers: Adidas and Nike each saw an increase of 1.20% in their share of search during Black Friday/Cyber Monday, highlighting their strong brand presence and consumer interest.
Top Losers: Reebok experienced a sharp decline, losing 2.60% in its share of search, while Levi’s also dropped by 0.60%.
Search Trends: The data suggests a strong consumer preference for activewear brands like Nike and Adidas and a decline in interest for traditional apparel brands like Levi’s.
Who Offered Most Value This Black Friday
In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 418 matched products across Apparel specific retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.
Here are the key takeaways from this analysis.
Category-Level Analysis
At the overall category level, Macy’s emerged as the lowest-priced retailer, offering the highest average discount of 28.72%, followed closely by Nordstrom (26.06%). The steep decline in average discounts from Saks Fifth Avenue (14.42%) and Neiman Marcus (7.93%) highlights a clear gap in discounting strategies.
Macy’s and Nordstrom are aggressively competitive on pricing in the overall apparel category, likely capturing consumer attention with substantial discounts.
Saks Fifth Avenue and Neiman Marcus may rely more on brand perception and luxury positioning rather than heavy discounting.
Subcategory-Level Analysis
Neiman Marcus tops the ranking with an impressive 60.85% average discount, outperforming Macy’s (52.86%) and Nordstrom (43.04%) for Men’s Clothing. We see a similar trend with Neiman Marcus offering more value across Women’s Clothing as well, compared to other retailers.
The competition in footwear was intense, with Neiman Marcus narrowly securing the top spot at 31.03%, slightly ahead of Saks Fifth Avenue (30.28%) and Macy’s (30.07%).
Saks Fifth Avenue led by a significant margin in the Activewear category, offering 39.89% average discounts, indicating a strong push in this growing segment.
Macy’s followed at 32.16% in Activewear, while Neiman Marcus and Nordstrom had comparatively lower discounts of 26.40% and 19.52%, respectively.
Brand-Level Analysis
Kate Spade New York: Neiman Marcus leads with the highest discount of 55.23%, reflecting strong price leadership in premium fashion, closely followed by Saks Fifth Avenue at 51.66%.
Coach: Neiman Marcus dominates with a significant 75.85% discount, showcasing an aggressive promotional strategy for this luxury brand.
Spanx: While Neiman Marcus leads with 28.22%, discounts across other retailers like Saks Fifth Avenue, Macy’s, and Nordstrom are clustered within a competitive range of 17–19%.
Montblanc: Macy’s takes the lead with 20.32%, signaling its competitiveness even in high-end accessories, with Saks Fifth Avenue and Nordstrom closely behind.
Ugg: Saks Fifth Avenue leads with 31.42%, focusing on maintaining price leadership for this popular brand, while other retailers remain competitive with discounts around 25–30%.
What’s Next
To win over price-conscious shoppers, retailers need to stay competitive and consistently offer the lowest prices.
For a deeper dive into the world of competitive pricing intelligence and to explore how our solutions can benefit apparel retailers and brands, reach out to us today!
Stay tuned to our blog for more insights on different categories this Black Friday and Cyber Monday.
As shoppers flocked online and to stores during Black Friday and Cyber Monday, the grocery category stood out as a key battleground for retailers. With inflation affecting consumer spending, discounted groceries have become a critical driver for both shopper savings and retailer competitiveness.
In fact, according to the NRF, one of the top shopping destinations during Thanksgiving weekend were department stores (42%), online (42%),and grocery stores and supermarkets (40%). Clearly, consumers are looking to stock up in bulk on their groceries to maximize their savings.
To understand the pricing dynamics in the grocery category, DataWeave analyzed grocery discounts across leading grocers, uncovering significant trends that shaped consumer choices during this holiday shopping period.
Our research encompassed retailers like Amazon, Target, and Walmart, examining their discounting strategies across subcategories, alongside trends in share of search for leading CPG companies.
Key Grocery Market Stats for Black Friday-Cyber Monday 2024
Retailer Discounts: Walmart offered the highest average absolute discount at 27.6%, followed by Amazon at 20.4% and Target at 14.0%
Subcategory Insights: Beverages Category at Walmart saw the deepest discounts, with an average of 33.4%
Top Gaining Brands: Cesar experienced the largest increase in share of search during the sales period (+3.89%)
This blog will dive deeper into grocery discount trends and brand-level strategies, offering insights for retailers looking to stay competitive in the grocery sector.
Our Methodology
For this analysis, we tracked the average discounts offered by major U.S. grocery retailers during the Thanksgiving weekend, including Black Friday and Cyber Monday. We focused on key subcategories within the grocery segment, capturing trends in discounting strategies.
In the following insights, the Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.
Key Findings
Retailer-Level Insights
Walmart emerged as the leader in grocery discounting, offering the highest average absolute (27.6%) and additional (18%) discounts.
Amazon adopted a mid-tier discounting strategy, with average absolute discounts of 20.4%.
Target, while more conservative, maintained competitiveness in select subcategories like baby products.
Subcategory Insights
Pantry Essentials saw Walmart leading with an average discount of 31.2%, appealing to budget-conscious consumers stocking up for the holidays.
Fresh Produce showed consistent discounting across retailers, with Amazon slightly ahead at 27%.
Beverages stood out for significant discounting at Walmart, with an impressive 33.4% average discount.
Brand-Level Insights
Lay’s led in absolute discounts (37.52%) and additional discounts (26.23%) showcasing aggressive pricing in the snacks subcategory.
Good & Gather maintained its competitive edge with strong discounts, appealing to price-conscious consumers seeking value.
Brands like Blue Buffalo (pet food brand) offered significant absolute discounts, but with a low additional discount of just 2%, the overall impact of the sale event on effective value was limited.
Share of Search Insights
Cesar (dog food brand), Tide (laundry staple) and Doritos saw significant gains in share of search, reflecting successful promotional strategies.
Brands like Pampers (baby diapers brand), Healthy Choice, (frozen foods brand) and Pedigree (pet food brand) experienced a decline, indicating less effective engagement during the sale period.
Who offered the lowest prices?
In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 1433 matched products across retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.
Here are the key takeaways from this analysis.
Category-Level Analysis
Walmart is the lowest priced retailer overall for the grocery category, with an impressive average discount of 44.60%. This significant discount advantage makes Walmart a leading option for value-seeking consumers.
Target follows with strong discounts of 36.73%, indicating solid pricing in comparison but less aggressive than Walmart.
Interestingly, Amazon was the most expensive in Grocery, with an average discount of only 6.3%.
Subcategory-Level Analysis
Walmart leads in various subcategories such as Pet Products (21.12%), Dairy & Eggs (13.79%), Household Essentials (13.05%), Frozen Foods (15.07%), and Meat & Seafood (17.60%), showcasing its extensive value across the board.
Target excels in Beverages (14.58%) and Baby Products (15.00%) with competitive discounts, standing out in these specific subcategories.
Kroger provides notable value in Pantry Essentials (20.04%) and Fresh Produce (15.85%), although its overall average discount is lower than Walmart’s.
Amazon consistently ranks lower in terms of average discounts across most subcategories, highlighting it as less competitive for consumers seeking the lowest prices.
Brand-Level Analysis
Walmart also holds the top position for several key brands like Cheetos (14.92%) and Dannon (8.81%), making it the best option for consumers looking for budget-friendly choices across popular brands.
Target takes the lead for brands like Betty Crocker (25.20%) and Chobani (11.37%), showing that it can offer value for specific products.
Kroger maintains strong discounts for brands such as Delmonte (9.19%), but it does not outpace Walmart in the overall grocery brand comparison.
Amazon generally lags behind in average discounts for most brands, with Dannon (1.12%) and Chobani (2.43%) showing significantly lower discounts.
Walmart is the lowest priced retailer in the grocery category and provides substantial value across a wide range of subcategories and popular brands. This ties in with Walmart’s ELDP pricing strategy. The retailer leads in overall average discounts and maintains its position as the go-to for price-conscious consumers. Target offers strong value in certain subcategories and brands but falls short of Walmart’s broad value based pricing advantages.
What’s Next
For grocery retailers, competitive pricing and targeted promotions are critical to driving sales during key shopping events. As consumers continue to prioritize value, staying ahead in the discounting game can significantly impact market share.
For detailed insights into grocery discounting strategies and to explore how DataWeave’s solutions can help retailers optimize their pricing, contact us today!
Stay tuned to our blog for further analyses of other categories during Black Friday and Cyber Monday.
The Home & Furniture category continues to thrive, propelled by consumer interest in creating personalized and functional living spaces. In 2023, the U.S. furniture and home furnishings market was valued at approximately $641.7 billion in 2023 and is estimated to grow at a CAGR of 5.1% from 2024 to 2032. Black Friday and Cyber Monday play a crucial role in fueling this growth, offering consumers a mix of premium and affordable options across subcategories.
To better understand market trends and discount strategies this Black Friday, at DataWeave we tracked over 18,149 SKUs across major home & furniture retailers, including Amazon, Walmart, Target, Best Buy, Home Depot, and Overstock, from November 10 to 29, 2024. Using our AI-powered pricing intelligence platform, we focused on the top 500 products in subcategories like kitchenware, furniture, decor, lighting, outdoor items, and bedding.
In our analysis, the Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the Black Friday sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.
Also check out our insights on discounts and pricing for the health & beauty category this Black Friday.
Retailer Performance: Who Led the Discount Race?
Retailers showed varying discount strategies for Home & Furniture products. Walmart emerged as the leader in absolute discounts (37.5%) while Amazon offered the highest additional discount of 14%. Best Buy maintained competitive pricing across all subcategories, while Overstock and Home Depot offered relatively modest discounts.
Subcategories in Focus
Breaking down the discounts by subcategory provides deeper insights into consumer priorities and retailer strategies:
Kitchenware saw strong competition, with Walmart (30.40% absolute discounts) and Amazon (29% absolute discounts) dominating.
Lighting became a discount hotspot, with Walmart offering up to 45.8% in absolute discounts and 25.3% additional markdowns.
Furniture remained a core focus for Target, delivering an impressive 34% average absolute discount.
Bedding stood out at Walmart, where discounts peaked at 49.6%.
Brand Spotlight: Who Stood Out?
Among top-performing brands, furniture brand Costway offered the highest discounts, with an average of 48.4%. Meanwhile, Adesso (lighting solutions), Mainstays and Safavieh (both home furnishings brands) balanced discounts and premium appeal.
Search Visibility: The Winners and Losers
Share of search dynamics revealed significant shifts in brand visibility during Black Friday:
Furniture brand Costway (+1.2%) and home improvement player Black+Decker (+1.5%) gained visibility.
On the flip side, premium brands like Safavieh known for rugs and home furnishings (-16.8%) and furniture brand Burrow ( -1.7%) saw declines.
Who Offers the Lowest Prices?
In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 735 matched products across Home & Furniture specific retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.
Here are the key takeaways from this analysis.
Category-Level Highlights
Amazon emerges as the lowest-priced retailer across Home & Furniture categories, with the highest average discount of 27.50%, closely followed by Walmart (26.09%).
Overstock and Wayfair trail with average discounts of 22.93% and 20.71%, respectively, while Home Depot offers the least aggressive pricing at 18.14%. This is notable, as all 3 players are known specialists in the category.
Subcategory Highlights
Amazon stands out as the leader in multiple subcategories, including Appliances, Furniture, Decor, and Outdoor, offering competitive average discounts of around 26-29%.
Overstock leads in Bedding and Kitchenware, with strong average discounts of 24.26% and 20.72%, respectively.
Wayfair is notable for Lighting, with an average discount of 19.95%, and is also competitive in Outdoor and Furniture categories.
Walmart consistently ranks high in several subcategories like Appliances and Bedding, providing solid discounts of around 22-23%.
What’s Next
For home & furniture retailers, driving maximum value during mega sale events like Black Friday involves offering bundles and sets to meet customer demands and trend expectations. Gaining insights into competitor discounts and pricing can help furniture retailers get an edge amid this environment.
Want to know how DataWeave’s intelligence platform can empower your business during peak sales events? Contact us to discover more about competitive insights, price intelligence, and data-driven decision-making. Stay tuned to our blog to see more coverage on Black Friday 2024.
The U.S. health and beauty retail sector shows remarkable resilience amid economic uncertainties, with the skincare market projected to hit $21.83 billion in 2024. Black Friday data reinforces this trend, with health and beauty products seeing a 14.6% surge in web traffic compared to last year.
At DataWeave, we conducted an in-depth analysis of Black Friday discounting trends in the U.S. health and beauty sector. DataWeave’s AI-powered pricing intelligence platform was used to monitor pricing and discounts across Sephora, Ulta Beauty, Walmart, Target, and Amazon during Black Friday 2024. The study covered 19985 SKUs from November 10-29. We focused on the top 500 products ranked for each search keyword on each retail site, using targeted terms aligned with categories like “skincare” and “fragrance”.
The results? Beauty leads across categories in discount depth this year, with some retailers offering significant markdowns.
The Beauty Boom: More Than Just Looking Good
If there’s one thing the pandemic taught us, it’s that self-care isn’t just a luxury – it’s a necessity. This Black Friday proved that beauty has become an indispensable part of consumers’ lives, with retailers offering unprecedented discounts and crafting strategic promotions to capture the growing demand.
The Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.
Ulta Beauty led with 45% average discounts, followed by Sephora at 38.1% and Walmart at 35.2%. In terms of additional Black Friday discounts, Ulta maintained dominance at 35%, with Sephora following at 28%.
Hair care emerged as the standout category, with Ulta Beauty offering up to 56% discounts, reflecting sustained demand for at-home beauty routines. Skincare saw fierce competition, with Sephora emphasizing premium discounts (37%) while Walmart focused on value pricing (32.5%).
Fragrance and Makeup attracted consumers with targeted promotions from Walmart and Ulta Beauty, signaling strong demand for gifting items.
Major beauty brands echoed the sentiment. Premium skincare brand Clinique leads with 50.6% average discounts. Meanwhile, drugstore staples like Revlon (29.1%) and Maybelline (24.4%) balanced accessibility and affordability, driving mass-market appeal. Popular beauty and makeup brand L’Oreal Paris also offered a modest 22.8% average discount, reinforcing its position as a value-oriented brand.
The more interesting story? The massive shift in brand visibility, as our share of search rankings denote:
Shampoo and hair care brand Tresemmé saw an unexpected 5.5% jump in the share of search results
Beauty brand Herbal Essences gained 5.1% in share of search well
Declines in share of search were noted for brands like L’Oreal Paris (-1.8%) and Pantene (-0.6%), indicating missed opportunities in promotional visibility.
Insight: What’s driving this beauty boom? TikTok and social media continue to fuel beauty purchases, with viral products driving significant search and sales spikes. Plus, the “skinification” of hair care has turned basic shampoo shopping into a full-blown beauty ritual.
Who Offered the Lowest Prices?
In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 1133 matched products across Health & Beauty specific retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.
Here are the key takeaways from this analysis.
Bloomingdale’s emerges as the overall leader, offering the highest average discount of 14.87%, closely followed by Bluemercury (12.41%).
Ulta Beauty ranks third (10.94%), demonstrating competitiveness across key subcategories, while Sephora trails with the lowest average discount (7.33%), reflecting a more premium positioning.
Ulta Beauty leads in Hair Care with the highest discount (22.62%), while Bluemercury dominates in Skin Care (13.81%), Makeup (22.98%), and Fragrance (10.6%).
Sephora consistently offers the lowest discounts across all subcategories, reflecting their premium positioning.
Bluemercury offers the lowest prices for luxury brands like Kiehl (27.02%) and Laura Mercier (34.87%), with Bloomingdale’s closely trailing.
Bloomingdale’s leads for Bumble and Bumble (13.59%) and Hourglass (23.41%), showcasing strong promotional efforts.
Sephora maintains a more restrained discount strategy, with notable leadership only for Estée Lauder (7.18%).
Ulta Beauty shines in offering the steepest discount for Briogeo (33.26%), emphasizing competitiveness in key brands.
What’s Next for Holiday Discounting?
For retailers, the message is clear: traditional holiday playbooks need a serious update. For shoppers, it means unprecedented opportunities to score deals in categories that traditionally held firm on pricing.
Want to stay ahead of retail trends and optimize your holiday shopping strategy? DataWeave’s commerce intelligence platform helps brands and retailers strategically navigate these shifts. Contact us to learn more about how we can help you make data-driven decisions in this rapidly evolving retail landscape.
Stay tuned to our blog for forthcoming analyses on pricing and discounting trends across a spectrum of shopping categories, as we continue to unravel the intricacies of consumer behavior and market dynamics.
Black Friday, once confined to a single weekend, has evolved into a shopping season that now stretches well before Thanksgiving. With inflation hovering around 3% and consumer confidence showing signs of recovery, retailers are adapting their promotional calendars to capture early-bird shoppers and maintain a competitive edge.
Major retailers, including Amazon, Walmart, Target, and Best Buy, have capitalized on this trend by launching promotions weeks in advance, signaling the traditional holiday rush is now a month-long event. At DataWeave, we put these deals under a microscope.
Our Methodology
Using DataWeave’s advanced, AI-powered pricing intelligence platform, we tracked early Black Friday deals across Consumer Electronics, Home & Furniture, Health & Beauty, and Apparel categories. We monitored dedicated Black Friday deal pages on Amazon, Walmart, Target, Best Buy, Nordstrom, Neiman Marcus, and Sephora to gather and analyze discount data a week prior to Black Friday weekend.
Who’s Offering the Best Deals Across Categories?
Our pre- Black Friday analysis reveals a clear pattern of premium brands offering deeper discounts across categories ahead of the holiday. Here are some key findings around retail players:
Walmart emerges as the most aggressive discounter across categories, leading in Health & Beauty (57.07%), Apparel (48.97%), and Consumer Electronics (43.35%).
Amazon maintains consistent but lower discounts (28-29%) across categories, suggesting potential deeper cuts ahead.
Best Buy and Sephora, both category specialists, play it conservative compared to mass retail players.
Let’s look at each category more closely to get a detailed snapshot of the deals this Thanksgiving week:
Health & Beauty
Our analysis reveals that it’s not electronics, but the health & beauty category that leads with the widest discount range pre Black Friday, making it the category to watch out for.
Walmart takes the lead with an aggressive 57.1% average discount in this category, capitalizing on its value-oriented reputation.
Beauty specialist Sephora holds modest beauty discounts (32.81%) compared to other retailers.
Amazon offers the broadest range of SKUs (571) in the category.
Among the health & beauty brands we analyzed, cosmetics brand Tarte and viral K-Beauty skincare brand COSRX stand out with discounts above 40%, appealing to cost-conscious beauty enthusiasts.
Consumer Electronics
Our pre- Black Friday analysis reveals interesting insights about consumer electronics deals this season.
Walmart, once again, emerges as the frontrunner in the category with 43.4% average discounts.
Best Buy plays it conservative in electronics (30.75%), despite being a category specialist, but offers the most extensive SKU coverage (3030).
Amazon’s consistent 29.7% discount across 1,749 SKUs suggests they’re probably holding back their best deals for Prime members during Black Friday.
Brand-specific data for the category reveals significant deals on Speck (48.07%) and smart TV brand Insignia (39.22%), making accessories and mid-tier electronics attractive for early shoppers. Core computing (HP at 32.14%) and electronics brands maintain more conservative discounts. It remains to be seen if this changes on Black Friday or Cyber Monday.
Apparel
Our analysis of the apparel category reveals several highlights:
In the apparel category too, Walmart dominates with an impressive 49% average discount, effectively targeting price-sensitive shoppers in the fashion segment.
Nordstrom and Neiman Marcus, both known for apparel, offer significant discounts at 43.2% and 37.8% respectively.
Amazon’s expansive SKU coverage (1344) is countered by a modest 29.5% discount, showing its focus on variety over depth of discounts.
Premium fashion brands dominate the highest discounts this Black Friday in the apparel category. Vince Camuto leads with over 45.1% average discount. Notably, Levi and Nike’s aggressive 44.43% and 43.50% discounts suggests significant inventory positions or intent to capture market share.
Home & Furniture
Our analysis reveals an interesting trend across the category.
In the home & furniture category too, Walmart leads at 41.8% average discounts. Target follows closely, but with significantly lesser SKUs on offer.
Amazon’s 28.1% discount, though the lowest among major players, spans a substantial 1,982 SKUs, reinforcing its position as a marketplace for diverse needs.
Top 3 Products With the Highest Discounts Across Retailers
To provide a clearer picture of the early Black Friday landscape, we analyzed the top 3 products with the most substantial discounts in consumer electronics and health & beauty categories. These insights highlight how retailers are leveraging strategic discounts on high-value items to attract early shoppers.
Top Discounted Products in Consumer Electronics
Premium TVs dominate the discount scene, with LG’s 83″ OLED offering up to 44.5% off on Amazon, closely followed by a 44.4% discount on Best Buy, showcasing aggressive competition. The same product has much lower discounting on Walmart, but notably, the product is retailed at $3999.9, at least $1000 less than other retailers, highlighting Walmart’s commitment to offering lowest prices.
Gaming consoles, like the PlayStation 5 Slim Bundle, show moderate discounts (ranging from 15% on Walmart and Target to 25% at Best Buy), appealing to tech-savvy shoppers.
Notable competition is evident in price matching across major retailers, particularly in TVs and high-value electronics like the Nikon Z 8 camera, where Walmart offers the deepest discount at 13.75%, edging past Amazon and Best Buy.
Top Discounted Products in Health & Beauty
Viral skincare staples like Tatcha’s Water Cream show tight discounting consistency, with Walmart offering 19.47% off compared to Amazon’s 20% and Sephora’s 20.83%.
Trending haircare brand Olaplex displays greater disparity, with Walmart leading with a 33.33% discount, surpassing Amazon and Sephora. Luxury brand, Yves Saint Laurent’s Satin Lipstick is one of the highest discounted items across retailers.
Looking Ahead
Our analysis suggests that while some early deals offer genuine value, particularly in premium beauty and high-end electronics, many retailers might be holding their best discounts for Black Friday.
For shoppers, the key is being selective: jump on premium brand discounts now (since they’re likely to remain the same though the weekend), but wait on mid-range electronics and home goods where better deals are likely to emerge on Black Friday or Cyber Monday.
For retailers, the imperative is clear: dynamic pricing intelligence is crucial for maintaining a competitive edge while protecting margins. Competitive insights will be critical as the holiday season progresses to balance market share against profitability.
Stay tuned for our Black Friday Cyber Monday analysis next week, where we’ll track how these early discounts compare to the main event’s deals!
When egg prices surged 70% during the 2023 avian flu outbreak, grocery retailers faced a critical dilemma: maintain margins and risk losing customers, or absorb costs and watch profits evaporate. Similarly, rising olive oil and chocolate prices also had domino effects, cascading down from retailers to consumers. In each of these scenarios, those with sophisticated pricing intelligence systems adapted swiftly, finding the sweet spot between competitiveness and profitability. Others weren’t so fortunate.
This scenario continues to play out daily across thousands of products in the grocery sector. From breakfast cereals to fresh produce to bottled water, retailers must orchestrate pricing across a variety of categories – each with its own competitive dynamics, margin requirements, and price sensitivity patterns.
The Evolution of Grocery Pricing Intelligence
Imagine these scenarios in the grocery industry:
Milk prices spike during a supply shortage.
Your competitor drops egg prices by 20%.
Fresh produce costs fluctuate with an unseasonable frost.
For grocery retailers, these aren’t occasional challenges—they’re Tuesday. Reacting to each pricing crisis as it comes isn’t just exhausting—it’s a recipe for shrinking margins and missed opportunities.
Think of it this way: If you’re constantly playing defense with your pricing strategy, you’re already two steps behind. Commoditized items like milk and eggs face intense price competition, while seasonal products and fresh produce demand constant attention. Simply matching competitor prices or adjusting for cost changes isn’t enough anymore. What’s needed is a proactive approach that anticipates market shifts before they happen and turns pricing challenges into competitive advantages. This is where price management comes in.
Price management has transformed from simple competitor checks into a strategic power play that can make or break a retailer’s market position. Weekly manual adjustments have given way to a long-term strategic view, driven by data analytics and market intelligence. Here are the basics of how price management in grocery retail works today.
Three Pillars of Grocery Price Management
1. Smart Data Collection: Building Your Foundation
The journey begins with comprehensive data collection and storage across your entire product ecosystem. This means:
Complete Coverage Of All SKUs Across All Stores: Tracking prices for all SKUs across all stores, with particular attention to high-velocity items and volatile categories.
Dynamic Monitoring: Tracking prices across different time frequencies as grocery prices are highly volatile for different categories. So daily tracking for volatile items like dairy and produce, and weekly for more stable categories may be needed.
Competitive Intelligence: Gathering data not just on prices, but on promotions, pack sizes, and private label alternatives.
Infrastructure to Support Large Volumes of Data: Partnering with external data and analytics providers to bridge the gap when retailers struggle with the scale of digital infrastructure these data sets require.
2. Intelligent Data Refinement: Making Sense of the Numbers
Raw data alone isn’t enough—it needs context and structure to become actionable intelligence. This is called Data Refinement—the process of establishing meaningful relationships within the data to facilitate the extraction of valuable insights. This refinement stage is closely tied to the data collection strategy, as the quality and depth of the insights derived depend on the accuracy and coverage of the collected data.
Data refinement includes several key processes:
Advanced Product Matching
Picture this: You’re tracking a competitor’s pricing on organic apples. Simple, right? Not quite. Yes, Universal Product Codes (UPCs) and Price Lookup Codes (PLUs) are present in Grocery to standardize product identification across different retailers—unlike the fashion industry’s endless style variations. Still, product matching isn’t as straightforward as scanning barcodes.
Here’s the catch: many retailer websites don’t display them. Then there’s the private label puzzle—your “Store’s Best” organic apples need to match against competitors’ house brands, each with their own unique UPC. Throw in different sizes (4 Apples vs. 1Kg of Apples), regional product names (fancy naming for plain old arugula), and international brand variations (like the name for Sprite in the USA and China), and you’ve got yourself a complex matching challenge that would make conventional pricing intelligence providers sweat.
Custom Product Relationships for Consistent Pricing and Competitive Positioning
Think like a shopper browsing the dairy aisle. You regularly buy your family’s favorite organic yogurt, the 24oz tub. But today, you notice the larger 32oz size is on sale – except the 24oz isn’t. As you stand there, confused, you wonder: Is the sale only for the bigger size? Did I miss a promotion? Should I buy the 32oz even though it’s more than I need?
For shoppers, this inconsistent pricing across product variations creates a frustrating experience. Establishing clear relationships between related items in your catalog is essential for maintaining consistent pricing and a coherent competitive strategy.
Start by linking products based on attributes like size, brand, and packaging. That way, when you adjust the price of the 32oz yogurt, the 24oz version automatically updates too – no more scrambling to ensure uniform pricing across your assortment. Similarly, products of the same brand but with flavor variations should be connected to keep pricing consistent.
Taking this one step further, mapping your competitors’ exact and similar products is crucial for comprehensive competitive intelligence. Distinguishing between premium and private label tiers, national brands, and regional players gives you a holistic view of the landscape. With this understanding, you can hone your pricing strategies to maintain a clear, compelling position across your entire category lineup.
Consistent pricing, whether across your own product variations or against competitors, provides clarity and accuracy in your overall competitive positioning. By establishing these logical connections, you avoid the customer confusion of seemingly random, inconsistent discounts – and ensure your pricing strategies work in harmony, not disarray.
The Role of AI and Data Sciences in Data Refinement
On the surface, linking products based on attributes like size, brand, and packaging seems like a no-brainer. But developing and maintaining the systems to accurately and automatically identify these connections? That’s a whole different animal.
Think about it – you’re not just dealing with text-based product titles and UPCs. There are images, videos, regional variations, private labels, and a whole host of other data types and industry nuances to account for.
Luckily, DataWeave is one of the few companies that’s truly cracked the code. Our multimodal AI models are trained to process all those diverse data formats – from granular product specs to zany regional produce names. And it’s not just about technology; we also harness the power of human intelligence.
See, in the grocery world, category managers are the real decision makers. They know their shelves inside and out and can spot those tricky connections in product matching, especially when they are not UPC-based. That’s why DataWeave built in a Human-in-the-Loop (HITL) process, where their AI systems continuously learn from expert feedback. It’s a feedback loop that allows our customers to pitch in and keep product relationships accurate, reliable, and always adapting to new market realities.
So while product mapping may seem straightforward on the surface, the reality is it takes some serious horsepower to do it right. Thankfully, DataWeave has both the technical chops and the grocery industry know-how to make it happen. Because when it comes to pricing intelligence, getting those product connections right is half the battle.
3. Strategic Implementation: Turning Insights into Action
The true value of pricing intelligence (PI) is realized through its strategic application. Although many view PI as a technical function, its strategic significance is increasing, particularly in the context of recent economic pressures like inflation. Here’s why:
Tactical vs Strategic Use of Data: From Standard Reporting to Competitive Analysis
Pricing intelligence has come a long way from the days of simply reacting to daily price changes. These days, it’s not just about firefighting—it’s about driving long-term strategy.
You can use pricing data to make quick, tactical adjustments, like matching a competitor’s sudden price drop on milk. Or, you can leverage that same data to predict market trends, optimize your product lineup, and shape your overall pricing strategy. Retailers who take that strategic view can get out ahead of the curve, anticipating shifts instead of just chasing them.
DataWeave supports both of these approaches. Our Standard Reporting tools give pricing managers the nitty-gritty details they need—current practices, historical patterns, and operational KPIs. It’s all the insights you’d expect for making those tactical, day-to-day tweaks.
In addition, DataWeave offers something more powerful: Competitive analysis. This is where pricing intelligence becomes a true strategic weapon. By providing a high-level view of market positioning, competitor moves, and untapped opportunities, competitive analysis empowers leadership to make proactive, big-picture decisions.
Armed with this broader perspective, retailers can start taking a more surgical approach. Maybe you need to adjust pricing zones to better meet customer demands. Or rethink your overall strategies to stay ahead of the competition, not just keep pace. It’s the difference between constantly putting out fires and systematically fortifying your entire pricing fortress.
Beyond Pricing: Comprehensive Data for Broader Insights
Pricing intelligence is just the tip of the iceberg. When you really start to refine and harness your data, the possibilities for grocery retailers expand far beyond simple price comparisons. Think about it – all that information you’re collecting on products, markets, and consumer behavior? That’s a goldmine waiting to be tapped. Sure, you can use it to keep a pulse on competitor pricing. But why stop there?
What if you could leverage that data to optimize your product assortment, making sure you’re stocking the right mix to meet customer demands? Or tap into predictive analytics to get a glimpse of future market shifts, so you can get out ahead of the curve? How about using it to streamline your supply chain, identify availability inefficiencies, and get products to shelves faster?
Sure, pricing intelligence will always be mission-critical. But when you couple it with these other data-driven insights, that’s when grocery retailing gets really interesting. It’s about evolving from a price-matching robot to a true strategic visionary, armed with the intelligence to take your business to new heights.
Looking Ahead: The Future of Grocery Pricing Intelligence
The grocery pricing landscape continues to evolve, driven by:
Integration of AI and machine learning for predictive pricing
Enhanced focus on omnichannel pricing consistency
Growing importance of personalization in pricing strategies
Pricing intelligence isn’t just about having data—it’s about having the right data and knowing how to use it strategically. Success requires a comprehensive approach that combines robust data collection, sophisticated analysis, and strategic implementation.
By embracing modern pricing intelligence tools and strategies, grocery retailers can navigate market volatility, maintain competitive positioning, and drive sustainable growth. The key lies in building a pricing ecosystem that’s both sophisticated enough to handle complex data and flexible enough to adapt to changing market conditions.
Ready to transform your pricing strategy? Check out our grocery price tracker to get month-on-month updates on grocery prices in the real world. Contact us to learn how our advanced pricing intelligence solutions can help your business stay ahead in the competitive grocery market.
As summer winds down, families across the US have been gearing up for the annual back-to-school shopping season. The back-to-school season has always been a significant event in the retail calendar, but its importance has grown in recent years. With inflation still impacting many households, parents and guardians are more discerning than ever about their purchases, seeking the best value for their money.
The National Retail Federation has forecasted that this season could see one of the highest levels of spending in recent years, reaching up to $86.6 billion. As shoppers eagerly stock up on back-to-school and back-to-college essentials, it’s crucial for retailers and brands to refine their pricing strategies in order to capture a larger share of the market.
To understand how retailers are responding to the back-to-school rush this season, our proprietary analysis delves into pricing trends, discount strategies, and brand visibility across major US retailers, including Amazon, Walmart, Kroger, and Target. By examining 1000 exactly matching products in popular back-to-school categories, our analysis provides valuable insights into the pricing strategies adopted by leading retailers and brands this year.
Price Changes: A Tale of Moderation
The most notable trend in our analysis is the much smaller annual price increases this year, in contrast to last year’s sharp price hikes. This shift is a reaction to growing consumer frustration about rising prices. After enduring persistent inflation and steep price growth, which peaked last year, consumers have become increasingly frustrated. As a result, retailers have had to scale back and implement more moderate price increases this year.
Kroger led the pack with the highest price increases, showing a 5.3% increase this year, which follows a staggering 19.9% rise last year. Walmart’s dramatic price increase of 14.9% is now followed by a muted 3.1% hike. Amazon and Target demonstrated a similar pattern of slowing price hikes, with increases of 2.3% and 2.7% respectively in the latest period. This trend indicates that retailers are still adjusting to increased costs but are also mindful of maintaining customer loyalty in a competitive market.
When examining specific product categories, we observe diverse pricing trends. Electronics and apparel saw the largest price increases between 2022 and 2023, likely due to supply chain disruptions and volatile demand. However, the pace of these increases slowed in 2024, indicating a gradual return to more stable market conditions. Notably, backpacks remain an outlier, with prices continuing to rise sharply by 22%.
Interestingly, some categories, such as office organization and planners, experienced a price decline in 2024. This could signal an oversupply or shifting consumer preferences, presenting potential opportunities for both retailers and shoppers.
Brand Visibility: The Search for Prominence
In the digital age, a brand’s visibility in online searches can significantly impact its success during the back-to-school season. Our analysis of the share of search across major retailers provides valuable insights into brand prominence and marketing effectiveness.
Sharpie and Crayola emerged as the strongest performers overall, with particularly high visibility on Target. This suggests strong consumer recognition and demand for these traditional school supply brands. BIC showed strength on Amazon and Target but lagged on Kroger, while Pilot maintained a more balanced presence across most retailers.
The variation in brand visibility across retailers also hints at potential partnerships or targeted marketing strategies. For instance, Sharpie’s notably high visibility on Target (5.16% share of search) could indicate a specific partnership.
Talk to us to get more insights on the most prominent brands broken down by specific product categories.
Navigating the 2024 Back-to-School Landscape
As we look ahead to the 2024 back-to-school shopping season, several key takeaways emerge for retailers and brands:
Price sensitivity remains high, but the rate of increase is moderating. Retailers should carefully balance the need to cover costs with maintaining competitive pricing.
Strategic discounting can be a powerful tool, especially for lesser-known brands looking to gain market share. However, established brands would need to rely more on quality, visibility, and brand loyalty.
Online visibility is crucial. Brands should invest in strong SEO and retail media strategies, tailored to different retail platforms.
Category-specific strategies are essential. What works for backpacks may not work for writing instruments, so a nuanced approach is key.
Retailers and brands should be prepared for potential shifts in consumer behavior, such as increased demand for value-priced items or changes in category preferences.
By staying attuned to these trends and remaining flexible in their strategies, businesses can position themselves for success in the competitive back-to-school retail landscape of 2024. As always, the key lies in understanding and responding to consumer needs while maintaining a keen eye on market dynamics.
Stay tuned to our blog to know more about how retailers can stay aware of changing pricing trends. Reach out to us today to learn more.
As the retail landscape continues to evolve, events like Amazon Prime Day have become more than just shopping extravaganzas—they’ve transformed into strategic battlegrounds where retailers assert their market positions and brand identities. Prime Day 2024 was no exception, serving as a crucial moment for retailers to showcase their pricing prowess, customer loyalty programs, and category expertise.
In an era where consumer expectations for deals are at an all-time high, the impact of Prime Day extends far beyond Amazon’s ecosystem. Retailers like Walmart, known for its “everyday low prices,” Target with its emphasis on style and value, and Best Buy, the electronics specialist, have all adapted their strategies to compete. These companies didn’t just react to Prime Day; they proactively launched their own pre-emptive sales events, with Target Circle Week, Walmart July Deals and more, effectively extending the shopping bonanza and challenging Amazon’s dominance.
For Prime Day, we analyzed over 47,000 SKUs across major retailers and product categories to publish insights on Amazon’s pricing strategies as well as the performance of leading consumer brands. Here, we go further to delve into the discounts offered (or not offered) by Amazon’s competitors during Prime Day. Our analysis reveals that some retailers chose to compete on price during the sale for certain categories, while others did not.
Below, we highlight our findings for each product category. The Absolute Discount is the total discount offered by each retailer during Prime Day compared to the MSRP. These are the discounts consumers are familiar with, displayed on retail websites prominently during sale events. The Additional Discount, on the other hand, is the reduction in price during Prime Day compared to the week prior to the sale, revealing the level of price markdowns by the retailer specific to a sale event.
Consumer Electronics
In the Consumer Electronics category, Best Buy stood out as a strong competitor, offering an Additional Discount of 5.9%—the highest among all competitors analyzed. This is unsurprising, as Best Buy is well-known for its focus on consumer electronics and is likely aiming to reinforce its reputation for offering attractive deals in order to maintain its strong consumer perception in the category.
Walmart was a close second with a 4.3% Additional Discount while Target reduced its prices by only 2% during the sale.
Apparel
In the Apparel category, Walmart’s Additional Discount was 3.1%, demonstrating its willingness to be priced competitively on a small portion of its assortment during the sale, without compromising much on margins.
Target, on the other hand, opted out of competing with Amazon on price during the sale, choosing instead to maintain its Absolute Discount level of around 11%.
Home & Furniture
The Home & Furniture category showcased diverse strategies from retailers. Specialty furniture retailers such as Overstock and Home Depot provided Additional Discounts of 3.9% and 2.5%, respectively, compared to Amazon’s 6.9%. This indicates a clear intent to maintain market share and remain top-of-mind for consumers despite Amazon’s competitive pricing.
Although Target didn’t significantly lower its prices during the sale, its Absolute Discount remains substantial at 18.9%. This suggests that Target’s markdowns were already steep before the event, which could explain the lack of further reductions during the sale.
Health & Beauty
The Health & Beauty category saw minimal participation from Amazon’s competitors, with the exception of Sephora, which reduced prices by 3.7% during Prime Day.
Ulta Beauty chose not to adjust its prices, likely reflecting its strategy to uphold a premium brand image. Walmart, on the other hand, offered a modest Additional Discount of 2% on select items. Given Walmart’s generally affordable product range, its total discount remained relatively low, around 3.5%.
In Conclusion
During Prime Day, Walmart was the only major retailer that made an effort to compete, albeit modestly. Target, on the other hand, largely chose not to offer any additional markdowns. However, several category-specific retailers, such as Best Buy in Consumer Electronics, Overstock and Home Depot in Furniture, and Sephora in Health & Beauty, aimed to retain market share by providing notable discounts.
What this means for consumers is that even on Amazon’s Prime Day, it’s not a bad idea to compshop to identify the best deal.
For retailers, the key takeaway is the importance of quickly analyzing competitor pricing and making agile, data-driven decisions to improve both revenues and margins. By utilizing advanced pricing intelligence solutions like DataWeave, retailers can optimize their discount strategies, better navigate pricing complexities, and drive revenue growth — all while staying prepared for major shopping events and beyond.
Our world is increasingly going online. We work online, socialize online, and shop online every day. As a consumer brand, you need to ensure complete awareness of your brand’s online presence across eCommerce platforms, search engines, and media.
Only by deeply understanding the customer journey can you ensure that your product is reaching your ideal customers and maximizing your brand’s market share. You need data to intrinsically understand your customer journey and make changes where you’re lacking.
As the old adage goes: ‘You can’t manage what you don’t measure.’
You need digital shelf metrics to measure and start benchmarking your buyer’s journey. To find several of these types of key performance indicators (KPIs), you need a digital shelf analytics solution. These platforms allow you to track various metrics along the path to purchase from the awareness stage to the post-purchase phase across the entire internet, helping to inform online and offline sales strategies.
Digital shelf analytics will help you gain insights into how your brand is doing versus the competition, which areas are lagging behind in historical performance, and what activities are driving sales. There are innumerable ways in which you can leverage these valuable insights. But how do you know which KPIs to start tracking with your digital shelf analytics solution?
Here, we’ve summarized the top metric types your peers report, track and base their decisions on.
With these KPIs in hand, consumer brands like yours can ensure that their products are consistently visible and appealing to their target audience across online marketplaces, ultimately enhancing conversion rates, market share, and profitability.
Read this guide to learn more about the top digital shelf metrics consumer brands are tracking and how to use them in your own strategy.
1. Share of Search
Share of Search (SoS) is a KPI in digital shelf analytics that measures how frequently a consumer brand’s products appear in search results on eCommerce platforms relative to the competition for specific keywords. A good digital shelf analytics solution will be able to show this metric across all the top marketplaces and retailers, such as Amazon and Walmart, but also more niche marketplaces for industry-specific selling.
This metric provides brands with a quantifiable way to measure how frequently their products are being “served up” to customers on online marketplaces. Essentially, it measures visibility and discoverability.
With Share of Search on DataWeave, you can slice and dice your data in innumerable ways. These are a few important views you can see:
Aggregated SoS
Organic and Sponsored SoS scores
SoS scores across brands, retailers, keywords, cities
Historical SoS score trends
Once you have benchmarked your SoS and category presence relative to your competition, you need to start interpreting the data. Here are some questions you can ask yourself to help interpret your findings:
Which of my key categories have the lowest SoS score?
Which products feature low on search results because they are out of stock?
Are my competitors’ products faring better due to sponsored searches?
Is my SoS low due to poor content quality?
With insights in hand, you will know which actions to take to drive the biggest impact. For example, you could increase sponsored search results or improve organic reach by optimizing product pages.
Understanding your SoS is essential to maximizing the awareness phase of your customer journey. It will help you improve your brand visibility and increase product conversions through better search and category presence.
2. Share of Media
Share of Media (SoM) is a KPI that is just as impactful, if not more so, than the SoS metric. However, only a limited number of brands track it or use it to drive strategic action. This makes it a perfect opportunity for brands looking to get an edge on the competition.
But what is SoM in digital shelf analytics? Essentially, it’s a way of measuring retail media advertising activities like brand-sponsored banners, listings, videos, ads, and promotions that sometimes blend into search results. The main types of retail media advertising exist in two categories: banner advertising and sponsored listings.
Banner advertising involves strategically placing designed banners within websites and search listings. These banners raise brand awareness and drive traffic to online storefronts.
Sponsored listings are paid placements within search results on search engines or eCommerce platforms. They are prioritized based on the total bid amount and the product’s relevance. These paid listings are marked with “sponsored” or “ad.”
These listings serve to enhance your brand’s overall visibility, help you gain more precise reach, increase conversions, and drive better brand awareness and recall with your customers.
These efforts aren’t free, however, so measuring their effectiveness is critical not only to gain all the listed benefits but to also not waste your valuable marketing budget. The SoM KPI can help a consumer brand answer questions like:
Where are the opportunities to increase paid ads?
Which categories could benefit from a promotional boost or a strategic and streamlined allocation of ad spend?
Which of my competitors have active banners and what is their share of media by keyword?
How has my ad spend trended historically in comparison to my competitor?
DataWeave’s digital shelf analytics (DSA) is among the first providers to offer Share of Media KPI tracking and analysis. This is because it requires advanced, multi-modal AI to gather, view, and aggregate listings that encompass text, images, and video. With Share of Media tracking facilitated by DataWeave, consumer brands can track and analyze the effectiveness of their own promotional investments as well as those of their competitors.
3. Content Quality
The content quality metric measures how well your product content adheres to the retailer’s specific guidelines, which are in place to steer traffic and sales on their sites.
With the help of a DSA platform’s AI and ML capabilities, you can measure different elements of your product detail pages (PDPs), such as titles, descriptions, images, videos, and even customer reviews. You need to know which elements are missing, where they are missing, and which ones are negatively affecting sales so you can take corrective action.
Did you know that the average cart abandonment rate is 69.99%? The quality of your content can significantly impact this number. Ensuring that your content is high-quality will help influence product discoverability, customer engagement, and conversion rates. It will also help position you ahead of the competition. If your content quality is poor, you may find yourself with lower search rankings, a higher return rate, and more abandoned carts.
Here are some questions you can answer with the help of the content quality digital shelf metric:
Is my product content at a retail site exactly what was syndicated?
Are there any retailer initiated changes to my product content?
Are my product content updates reflected on the retailer platforms?
How well does my product content comply with the retailer guidelines?
How do I optimize my product content for enhanced discoverability and conversion?
DataWeave’s content quality digital shelf analysis helps consumer brands ensure that product content on eCommerce platforms is high-quality and benchmark their product listings against the competition. It does this through a combination of AI-driven quality analysis and by presenting brands with actionable recommendations. These optimized suggestions are based on the top-performing products so you can focus your valuable time on the areas that will drive the biggest impact.
4. Pricing & Promotions
Your customers can easily shop around to find the best price for the product you’re selling. If your competitor is selling it cheaper, you’ll lose that sale.
That’s why it’s essential to understand the pricing and promotional landscape for each of your products and categories. This can be a challenge, especially if it’s a common product or comes in multiple pack sizes or variants.
It’s equally important to track pricing and promotions even at individual, physical stores. Doing so will allow you to remain competitive and responsive to local market dynamics by tailoring your pricing strategies based on regional competition. You don’t want your products to be overpriced (lost sales) or underpriced (lost profit) in specific markets.
Harmonizing insights when operating an omnichannel consumer brand is extremely difficult without the aid of a digital shelf analytics solution. Insights need to be aggregated between desktop sites, mobile sites, and mobile applications, as well as from physical storefronts.
Questions you can answer with the help of the pricing & promotions digital shelf metric include:
How do my product prices and promotions compare to my competitors?
How consistent is my product pricing across retail websites?
How does my product pricing vary across regions, ZIPs, and stores?
How do price changes influence my sales numbers?
Are there regional differences in pricing and promotion effectiveness?
DataWeave’s digital shelf analytics platform stands out with its sophisticated location-aware capabilities, which enable the aggregation and analysis of localized pricing and promotions. The platform defines locations based on a range of identifiers, such as latitudes and longitudes, regions, states, ZIP codes, or specific store numbers.
The platform can also extract promotional information, such as credit card-based or volume-based promotions. You can see variances across retailers, split by price groups, brands, and competitors. DataWeave specializes in enabling brands to conduct in-depth analyses across a wide array of attributes so you can answer just about any pricing or promotional question you have.
5. Availability
The availability KPI in digital shelf analytics measures the in-stock and availability rates for a brand’s products across eCommerce and physical locations. Similar to the pricing and promotions metric, it relies heavily on location awareness, down to individual stores. Measuring both online availability and offline in-stock rates will help you understand the big picture and take more informed replenishment action.
When you start leveraging the availability KPI with the help of digital shelf analytics, you can improve inventory management, boost product discoverability, increase the frequency with which your online product listings convert, and generally drive more sales. This KPI is essential for ensuring your customers can always find and buy the products they want.
With the availability KPI, you can start answering questions like:
What is my overall in-stock rate?
Which of my products frequently go out of stock?
How does product availability vary across different regions and stores?
What is the impact of availability on my conversion rates?
Are there any seasonal trends in product availability that I need to address?
How quickly are we resolving stockout issues across different locations?
What are my biggest opportunities to reduce stockouts?
DataWeave enables consumer brands to track their product availability metric through automated data collection from various eCommerce platforms in conjunction with physical in-stock rates. The platform provides granular, store-level insights so you can understand regional stock variations and optimize inventory distribution. By tracking historical availability data, you can identify seasonal patterns and predict future demand to pre-empt stockout issues. All of this can be configured with automatic notifications to alert you when there has been a stockout event or when a low stock threshold has been passed, facilitating timely replenishment.
6. Ratings & Reviews
The final KPI in our guide is the ratings & reviews digital shelf metric. Consumers rely heavily on genuine feedback from their peers and refer to star ratings, posted comments, and uploaded pictures to inform their buying decisions. This KPI analyzes the impact of customer feedback and reviews on your products’ performance across eCommerce platforms so you can measure overall brand perception and isolate areas of opportunity.
This metric does something other digital shelf metrics don’t; it can inform your product strategy. It can help you identify repeat complaints that your product team can address with the manufacturer or use for the design of future products.
Some questions you can answer with this powerful KPI include:
What is the overall customer sentiment towards my products based on ratings and reviews?
Which product features are frequently mentioned positively or negatively by customers?
How do my product ratings and reviews compare to those of my competitors?
Are there common issues or complaints that need to be addressed to improve customer satisfaction?
Which products have the highest and lowest ratings, and why?
With DataWeave’s digital ratings and reviews feature, you can keep a pulse on customer sentiment to take short-term action as well as decide long-term strategy. You can leverage reviews to influence product perception, refine products, and enhance overall customer satisfaction.
DataWeave’s Digital Shelf Metrics
Each one of these metrics is interconnected and collectively influences a brand’s success. For instance, improving content quality and earning higher ratings can significantly enhance your product’s visibility in search results, thereby boosting the Share of Search digital shelf metric. By focusing on a comprehensive approach that integrates these metrics, brands can ensure their products are consistently visible, competitively priced, well-reviewed, and readily available.
DataWeave gives consumer brands the means to execute a holistic digital shelf strategy. From a single portal, track and improve digital shelf metrics like Share of Search, Share of Media, Pricing and promotions, Availability, and Ratings and Reviews.
Our solutions help audit and optimize the most critical KPIs that drive sales and market share for brands so you can stay competitive in a dynamic digital landscape and foster long-term customer satisfaction.
As the dust settles on Amazon’s 8th Prime Day extravaganza in India, held on July 20-21, 2024, the eCommerce giant has once again shattered records. This year’s event saw unprecedented engagement, with 24% more Prime members participating compared to 2023, marking the highest-ever Prime member involvement in the sale’s history.
At DataWeave, we’ve conducted an extensive analysis of this landmark event, examining over 47,000 SKUs across major categories for Flipkart and Amazon to uncover compelling insights into pricing strategies, competitive positioning, and emerging trends in the Indian eCommerce space. This follows our comprehensive analysis of Prime Day in the USA across categories and brands, which readers can explore for a global perspective on Amazon’s strategies.
Key Highlights:
Tier-2 Cities Drive Growth: This year’s exponential growth was notably fueled by orders from tier-2 cities, highlighting the expanding reach of eCommerce in India.
Swift Deliveries: Most Prime members’ orders from metros were delivered the same day or the next, while tier-2 cities saw deliveries in less than 2 days, highlighting the importance of delivery time when it comes to purchase decisions.
Brand Participation: Over 450 top Indian and global brands participated, including Intel, Samsung, OnePlus, and Puma, alongside 3,200 new product launches from small and medium Indian businesses. This highlights Amazon’s policy to promote small, local businesses globally in 2024.
Premium Products in Demand: Despite modest discounts across electronics and similar offerings, consumers chose to purchase premium products during Prime Day. With financial flexibility and no-cost EMI options, Indian consumers, especially Gen Z and millennials, are now ready to invest in premium products and experiences.
Our analysis focused on both the Additional Discounts offered during Prime Day (calculated relative to prices the week prior to the event), highlighting the event’s genuine benefits, and the Absolute Discounts offered during Prime Day, representing the total markdown relative to the MRP.
Cross-Category Discount Strategy
Our analysis this year reveals that the Consumer Electronics category saw the highest Absolute Discounts with an average of 48.9%, closely followed by Apparel at 46.9%. The Health & Beauty category had the lowest Absolute Discount at 32.1%.
However, Amazon offered the highest Additional Discount in Health & Beauty at 9.3%. Electronics and Apparel had additional discounts of 8.6% and 7.7% respectively.
Overall, Flipkart chose to not compete aggressively with Amazon during Prime Day this year. Only in Electronics did Flipkart offer an additional discount of 3.2% during the sale. Price changes in Apparel and Health & Beauty were negligible.
Category Deep Dive
Consumer Electronics
In Electronics, Earbuds (14.7%) and Wireless Headphones (12.5%) saw the highest Additional Discounts on Amazon, likely due to high demand for personal audio devices. Smartwatches followed closely at 12.5%, indicating a strong push in the wearable technology sector.
Larger electronic appliances like TVs saw more modest Additional Discounts (5.2%), despite a significant Absolute Discount (42.3%).
Smartphones, interestingly, had the lowest Additional Discount (2.8%) among the subcategories, potentially due to their already competitive pricing or brand-specific strategies.
Brand-level Insights in Consumer Electronics
During the sale event, Soundcore (headphone and earphone brand from Anker) led with the highest Additional Discount of 10.3%, followed closely by OnePlus at 9.3%. These brands clearly leveraged Prime Day to drive sales and potentially gain market share.
Sennheiser and Sony also offered significant additional discounts, indicating aggressive promotional strategies during the event.
Notably, some well-known brands like Apple and HP offered more conservative discounts, possibly due to their established market positions or differing promotional strategies.
Despite modest discounts though, Amazon revealed that the iPhone 13 and OnePlus 12R were among the top premium smartphones customers bought this Prime Day. Prime members shopped from top brands such as LG, IFB, Bosch, and Haier and upgraded to premium appliances through affordability options of no cost EMI, Bank discounts and exchange offers, claims Amazon.
These learnings offer valuable insight into the mindset of the modern Indian consumer, who is ready to purchase premium products. Brands can leverage more detailed market intelligence to drive their positioning in India.
Share of Search Analysis
The Share of Search (SoS) for a brand represents the number of its products in the top 20 search results for relevant search keywords on Amazon, relative to that of its competitors. It provides insights into a brand’s visibility during the sale event.
HP saw the most significant improvement in its Share of Search during Prime Day, gaining 9.2%, with strong growth in both organic (7.5%) and sponsored (1.7%) listings. LG also made substantial gains of 8.2% and 5.3% respectively. Both brands offered significant markdowns on laptops and accessories.
Surprisingly, some popular brands like Apple (-0.6%) and Acer (-1.0%) experienced slight decreases in their SoS. Lenovo notably lost 7.2% of its visibility share, with a significant decline in organic listings (-7.6%). Samsung maintained its strong position with an increase in SoS (3.7%).
Key Takeaways:
While some brands like Soundcore and OnePlus used aggressive discounting to drive sales, others like HP and LG focused on improving their search visibility. The data suggests that a combination of attractive discounts and improved search visibility was key to success during the event. Brands that managed to balance these factors effectively, like HP and Samsung, seemed to perform particularly well. Interestingly, some brands with lower discounts (like HP) still managed to significantly increase their SoS, indicating that factors beyond pricing played a role in visibility during the event.
Apparel
Within the subcategories analyzed, Women’s Shoes (13.4%) and Men’s Shoes (11.6%) saw the highest Additional Discounts on Amazon, indicating a strong focus on footwear during the event. Men’s Shirts followed closely at 11.1%
Women’s Tops had a more modest Additional Discount of 7.9% despite a high Absolute Discount before the event. Similarly, Women’s Dresses showed substantial Additional Discounts (10.4%).
Swimwear for both men and women saw low Additional Discounts (3.7% and 3.0% respectively). Innerwear categories for both men and women had more conservative discounts, with Men’s Innerwear showing the lowest Absolute Discount (19.9%) among all subcategories.
Brand-level Insights in Apparel
During the sale event, Sangani (innerwear brand) led with the highest Additional Discount of 41%, followed by Louis Philippe at 25%. These brands clearly leveraged Prime Day to drive sales and gain market share.
Puma offered the highest Absolute Discount at 60% with a significant Additional Discount of 20%, indicating a consistently aggressive promotional strategy. Chromozome (men’s innerwear brand) and Bacca Bucci (homegrown shoe brand of Shark Tank India fame) also offered substantial Absolute Discounts (57% and 56% respectively), but their Additional Discounts varied significantly (16% and 6% respectively).
Again, some well-known brands like Adidas, and Pepe Jeans offered more moderate Additional Discounts, possibly due to their established market positions.
Share of Search Analysis
Biba (women’s ethnic wear brand) saw the most significant improvement in its SoS, gaining 3.60%, entirely through investments in sponsored listings. Allen Solly and New Balance (shoe brand) also made substantial gains of 3.3% and 3.2% respectively.
Surprisingly, some popular brands like Louis Philippe (-2.00%) and Sangani (-2.10%) experienced decreases in their SoS. Louis Philippe lost its share due to a roll back of its sponsored listings, while Sangini lost ground organically.
Key Takeaways:
While some brands like Sangani and Louis Philippe used aggressive discounting to drive sales, others like Biba and Allen Solly focused on improving their search visibility through sponsored listings.
Some brands with high discounts (like Sangani) saw a decrease in SoS, while others with more modest discounts (like Biba) significantly increased their visibility. This indicates that factors beyond pricing played a crucial role in brand performance during the event.
The significant use of sponsored listings by some brands highlights the importance of paid advertising in gaining visibility during competitive events like Prime Day.
Health & Beauty
Health & Beauty emerged as a standout category during Amazon Prime Day India 2024, with significant discounts and competitive strategies at play.
Within the subcategories we analyzed, Moisturizer (11.9%) and Sunscreen (11.8%) saw the highest additional price reductions on Amazon, likely due to seasonal demand during Prime Days. Make-up followed closely at 10.6%.
Electric Toothbrushes had the highest Absolute Discount at 47.1%, but a lower Additional Discount of 6.0%, highlighting that these products were already significantly discounted before the event. Similarly, Beard Care products showed high Absolute Discounts (43.7%) but lower Additional Discounts (5.7%) during Prime Day.
Everyday essentials like Toothpaste saw more modest discounts (19.9% Absolute, 8.4% Additional), reflecting steady demand for these products.
Brand-level Insights in Health & Beauty
Vaseline led with the highest Additional Discount of 13.1%, followed by Maybelline at 9.5%. These brands clearly leveraged Prime Day to drive sales and potentially gain market share.
Honest Amish (beard care brand) and Nivea also offered substantial Absolute Discounts (15.6% and 8.2% respectively), but their Additional Discounts were lower, indicating pre-existing promotional strategies.
Once again, some well-known brands like Colgate, Dove, and Neutrogena offered more conservative discounts, possibly due to their established market positions or differing promotional strategies.
Share of Search Analysis
Maybelline saw the most significant improvement, gaining 5.6% in SoS, all through organic listings. The brand has been aggressively increasing market share with several new product launches and influencer campaigns. Nivea and Vaseline also made substantial gains of 5.2% and 3.8% respectively.
In this category as well, some popular brands like Dove (-0.4%), Pond’s (-0.8%), and Honest Amish (-0.8%) experienced slight decreases in their SoS. Parodontax (toothpaste brand) notably lost 1.3% SoS, with most of the decline in sponsored listings. Colgate maintained its strong position with a slight increase in SoS (0.2%), reflecting its dominant market presence.
Key Takeaways:
While some brands like Vaseline and Maybelline used aggressive discounting and improved visibility to drive sales, others like Colgate and Dove relied more on their established market positions.
None of the brands made any significant strides in sponsored listings, revealing that brands in this category are not yet fully leveraging retail media to help manage and boost their visibility on online marketplaces.
Final Thoughts
Amazon Prime Day 2024 has redefined the eCommerce landscape in India, yet again, showcasing diverse strategies employed by brands to maximize their impact. By understanding and leveraging these insights, brands and retailers can better navigate future sales events, optimizing their eCommerce strategies to achieve sustained growth and success.
Stay tuned to our blog for more in-depth analyses of brand and retailer performance across various retail events.
Reach out to us today to learn how you can leverage data-driven insights to optimize your eCommerce strategy.
Amazon Prime Day 2024 saw U.S. shoppers spending a staggering $14.2 billion online during the two-day event—an 11% increase from last year. This surge in spending reflects a significant shift in consumer behavior and presents a wealth of insights for brands and retailers alike.
Unlike last year’s focus on essentials, Prime Day 2024 saw Americans enthusiastically embracing both necessities and discretionary purchases. The Consumer Electronics and Health & Beauty categories, for example, experienced a notable uptick in interest, driven by major retailers slashing prices across CPG and Grocery segments, amid other reasons. Check out our first article in the Prime Day series 2024, analyzing retail insights across categories during the event.
This year, small businesses gained unprecedented visibility on Amazon, pushing relatively new brands into visibility.
At DataWeave, we recognize the critical importance of understanding these market dynamics for brands navigating the competitive eCommerce landscape. To provide actionable insights, we conducted an extensive analysis of over 47,000 SKUs across key categories before and during Amazon during Prime Day 2024. Our study delves into:
Pricing strategies: How did brands adjust their discounts to capitalize on the Prime Day frenzy?
Share of Search: Which brands achieved the highest visibility for major search keywords?
Dive into these insights below to uncover how brands performed during Amazon Prime Day 2024, and learn how you can leverage these findings to enhance your brand’s digital shelf performance.
Our Findings
Most brands offered substantial discounts before Prime Day, then added smaller discounts during the event. This strategy creates a perception of value while still allowing room for Prime Day-specific deals. To understand the real value offered by brands, we conducted an extensive analysis of brand performance, examining both pricing strategies and visibility on the platform. Our approach focuses on two key metrics:
Discounts: We analyzed both the Absolute Discount (total markdown relative to MSRP a week before Prime Day) and the Additional Discount (the price reduction during Prime Day compared to the week before).
Share of Search (SoS): We examined the visibility of brands in the top 20 search results. We also separately tracked this metric for organic and sponsored search results.
Let’s dive into the category and brand specific insights:
Consumer Electronics
Once again, in 2024, the Consumer Electronics category dominated discounts. Amazon’s own brands lead with the highest average Absolute Discount (44.2%) and a significant Additional Discount (12.5%), showcasing its aggressive push for Prime Day.
In a surprising twist, Amazon’s homepage wasn’t dominated by its own brands. Instead, tech giants like Apple and Samsung took centre stage. Despite this, Amazon’s own brands offered significant discounts across electronics products, including Amazon Kindle, Fire TVs, Fire TV Sticks, Echo Dot, and more, aiming to capture market share via markdowns.
Soundcore (earphone audio products brand) offered the highest discount during Prime Day, at 30.10%. Other headphone, earbuds, and wireless headphone brands including Sony, Beats, JBL, and more also offered significant discounts.
Premium brands like Apple (17.90% Absolute, 9.00% Additional) and Bose (23.10% Absolute, 16.00% Additional) offered relatively modest discounts, aligning with their brand positioning, but also taking advantage of the Prime Day frenzy.
Share of Search Insights in Consumer Electronics
JBL emerged as the standout performer, with the most significant increase in SoS, jumping from 14.3% pre-event to 25.8% during Prime Day, driven entirely by organic growth. Beats also saw a remarkable rise, increasing from 2.8% to 12.8%, again through organic listings only. Samsung maintained its strong presence, growing from 18.6% to 26.8%, with most of its growth influenced by increased ad spend.
Apple, despite already having a high pre-event SoS, managed to increase its share further from 23.0% to 31.0%, with some contribution via sponsored ads. LG saw a substantial increase from 1.3% to 6.9%, primarily through sponsored listings, opting for an inorganic approach to drive visibility during the sale.
Amazon and its AmazonBasics brand both saw notable increases in SoS, relying solely on organic growth. This is, of course, not surprising since Amazon controls its organic ranking algorithm.
Interestingly, some brands experienced decreases in SoS. Sony, Motorola, and Hisense all saw reductions in their share, with Hisense’s decline coming entirely from a reduction in sponsored listings.
Key Takeaway: Prime Day 2024 saw a significant reshuffling of brand visibility in the Consumer Electronics category. While some established brands like JBL, Beats, Samsung, and Apple strengthened their positions through a mix of organic and sponsored growth, others faced increased competition for consumer attention. The event highlighted the importance of a balanced approach to visibility, with successful brands leveraging both organic search optimization and strategic use of sponsored listings to maximize their presence during this high-traffic period.
Apparel
In the Apparel category, Adidas led with the highest Absolute Discount (27.2%) and a significant Additional Discount (9.8%). Value brands like Hanes (innerwear brand) and Anrabess offered substantial discounts, while Amazon Essentials maintained high discounts across the board (16.5% Absolute, 15.3% Additional).
Some brands like Cupshe (swimwear and vacation apparel brand) offered relatively lower additional markdowns. Meanwhile CRZ Yoga (athleisure brand) did not offer additional markdowns on Prime Day.
Share of Search Insights in Apparel
Gildan (activewear brand) emerged as the top performer in terms of SoS growth, increasing from 8.1% pre-event to 12.1% during Prime Day, driven entirely by organic growth. CRZ Yoga (an athleisure apparel brand) and Dokotoo (women’s casualwear brand) also saw significant increases in their SoS, rising by 2.8 and 2.3 percentage points respectively, again through organic listings only.
Amazon Essentials continued to perform well, increasing its visibility from 7.0% to 9.1%, aligning with its competitive pricing strategy. Coofandy also saw a notable increase, growing from 7.8% to 9.9%.
Interestingly, some brands that were previously highlighted for growth actually experienced decreases in SoS. Automet (clothing & accessories brand) saw a slight decline from 12.5% to 12.1%, while Anrabess (women’s fashion brand) dropped from 15.8% to 14.5%. Cupshe (swimwear brand) experienced the most significant decrease, falling from 10.6% to 5.5%.
Adidas, despite leading in discounts, saw only a modest increase in SoS from 7.0% to 8.0%. Notably, none of the brands visible in the top search results utilized sponsored listings, with all changes in SoS coming from organic growth or decline. This indicates a lack of maturity in this category in leveraging retail media.
Key Takeaway: Prime Day 2024 in the apparel category showcased the importance of organic search optimization. While some brands like Gildan and CRZ Yoga significantly improved their visibility, others faced challenges in maintaining their pre-event positions. The absence of sponsored listings across all brands highlights a unique dynamic in the apparel category, where organic search performance appears to be the primary driver of visibility during high-traffic events like Prime Day.
This suggests that Apparel brands may need to focus more on SEO strategies and organic content optimization to maximize their presence during major shopping events, rather than relying on paid promotions. On the other hand, smartly leveraging retail media to boost visibility can give apparel brands a competitive edge.
Health & Beauty
The Health & Beauty category this year got a push thanks to Amazon’s subscription offering. Prime members who subscribed for regular usage products like toothpaste and health aids or medicines availed higher discounts.
Amid Health & Beauty brands, Neutrogena led with the highest Absolute Discount (32.7%) and a significant Additional Discount (11.7%). Sun Bum moisturizers & sunscreen (23.3%) and Viking Revolution (23.1%) offered the highest Additional Discounts, indicating a strong Prime Day focus.
Premium brands like L’Oreal Paris and Philips Sonicare offered moderate discounts, balancing promotions with their intended brand image.
Share of Search Insights in Health & Beauty
Banana Boat (sunscreen brand) emerged as the standout performer, seeing the largest increase in SoS from 6.5% to 13.4%, achieved entirely through organic growth. Nyx Professional Makeup also saw a significant jump, rising from 3.9% to 8.9%, again solely through organic listings.
Contrary to previous analysis, e.l.f. actually experienced substantial growth, increasing from 9.0% to 13.1% SoS, with a strong focus on organic growth (4.4%) slightly offset by a minor decrease in sponsored listings (-0.2%).
Neutrogena maintained its strong performance, aligning with its aggressive discounting strategy, as its SoS increased from 14.6% to 18.5% through organic growth. Colgate also saw a notable increase from 11.0% to 13.7% SoS.
Interestingly, some brands employed a mixed strategy. Dove and Garnier saw overall increases in SoS, but achieved this through different means. Dove relied heavily on sponsored growth, while Garnier offset a decrease in organic listings with strong sponsored content growth.
Contrary to previous observations, Oral-B experienced a decrease in SoS from 18.5% to 15.3%, entirely in organic listings. Without any additional spend on sponsored listings to compensate, it lost significant ground in its visibility. Other brands facing significant declines include Tresemme, OGX, Philips Sonicare, and most notably, Viking Revolution, which dropped from 17.2% to 10.7% in its SoS.
Key Takeaway: The Health & Beauty category during Prime Day 2024 showcased a diverse range of strategies and outcomes. While some brands like Banana Boat and Nyx Professional Makeup achieved significant visibility gains through organic growth, others like Dove and Garnier relied more on sponsored content.
The success of e.l.f. and Neutrogena in aligning discounting strategies with increased visibility stands in contrast to the challenges faced by previously strong performers like Oral-B and Viking Revolution. This varied landscape shows the fierce competition in the category and the need for brands to employ multi-faceted strategies that balance organic optimization, sponsored content, and competitive pricing to succeed in high-stakes events like Prime Day.
Brand Strategies and Future Implications
Our analysis reveals several key trends:
Brand Positioning Matters: Premium brands like Apple and Bose maintained their positioning with modest discounts, while value-oriented brands like Soundcore and Hanes offered deeper cuts to attract price-sensitive shoppers.
Visibility vs. Discounting: Some brands, particularly in the Consumer Electronics category, prioritized increasing their visibility (Share of Search) over offering steep discounts. This strategy suggests a focus on long-term visibility and brand perception rather than short-term sales boosts.
Category-Specific Approaches: Apparel brands uniquely relied on organic search visibility, eschewing sponsored listings entirely. In contrast, several Health & Beauty brands leveraged sponsored content significantly to boost their presence.
Emerging Brand Opportunities: Lesser-known brands, especially in the Apparel and Health & Beauty categories, used Prime Day as a launchpad to increase their visibility, often outpacing established names in Share of Search growth.
Amazon’s Dual Strategy: As both a platform and a brand, Amazon showcased its ability to offer deep discounts on its own products while also providing a stage for other brands to shine.
Stay tuned to our blog for more in-depth analyses of brand and retailer performance and strategies across various retail events. Reach out to us today to learn how you can leverage data-driven insights to optimize your brand’s eCommerce strategy and performance.
Amazon Prime Day 2024 has once again shattered records, with more items sold during the two-day event than any previous Prime Day. Prime members worldwide saved billions across all categories, while independent sellers moved an impressive 200 million items.
At DataWeave, we conducted an extensive analysis of the discounts offered by Amazon across major categories. By examining over 47,000 SKUs, we’ve uncovered compelling insights into pricing strategies, competitive positioning, and emerging trends in the eCommerce space.
Since products on Amazon and other eCommerce websites are often sold at discounts even on normal days not linked to a sale event, we delved into the real value that Prime Day offers to shoppers by focusing on price reductions or the Additional Discount during the sale compared to the week before. As a result, our approach highlights the genuine benefits of the event for shoppers who count on lower prices during the sale. At the same time, our report also includes the Absolute Discounts offered during Prime Day, which represents the total markdown relative to the MSRP.
Amazon’s Cross-Category Discount Strategy
Our analysis reveals that the Electronics category saw the highest discounts with an average absolute discount of 20.4% and additional discounts on Prime Day amounting to 10.4%. Meanwhile the Home & Furniture had the lowest discount at 13.1%.
The Health & Beauty category saw significant additional discounts during Prime Day, at 9.26%. The Apparel category offered attractive absolute (16.10%) and additional (8.90%) discounts.
Category Deep Dive
Consumer Electronics
Still the star of the show, the electronics category saw the highest markdowns this Prime Day with absolute discounts at 20.40% and across 14.61% of their inventory.
Across Electronics subcategories, Earbuds had the highest markdowns at 34.80%, followed closely by Wireless Headphones at 30.60% and Headphones at 29.00%, with steep additional discounts during Prime Day as well. Apple AirPods Pro, for example, retailed at $168 (down from $249) at a 32% discount.
Meanwhile, smartphones had the lowest markdowns at 9.30%, followed by Laptops at 10.50%. Laptops also had the lowest additional discount during Prime Day at just 1.28%, significantly lower than other subcategories. Speakers (20.80%), Drones (19.10%), and Smartwatches (25.00%) offered moderate to high markdowns.
Notably, all Amazon products including Kindle, Echo, Echo Earbuds, Alexa, Fire TV, Fire TV Stick, and Fire Tablets, were aggressively discounted upwards of 30% this Prime Day. These products also came with the label “Climate Pledge Friendly.”
These aspects indicate Amazon’s push to promote its own ecosystem of products to the top, as well as cater to changing consumer preferences.
Apparel
Discounts offered this Prime Day increased from 13.2% in 2023 to 16.1% in 2024.
Amid apparel subcategories, Amazon appears to be pushing Women’s apparel categories more aggressively, particularly in Tops, Shoes, and Athleisure.
Women’s Shoes lead with the highest discounts at 26.50%, followed by Women’s Tops at 22.50% and Men’s Shoes at 22.80%. Women’s Tops also maintained the highest additional discount at 15.27%, followed by Women’s Athleisure at 13.03% and Men’s Swimwear at 12.44%.
Similar to 2023, Men’s Innerwear offered significantly lower discounts, with only 1% absolute discount and 0.72% additional discount. Women’s Innerwear also shows low discounts at 3.20% absolute and 2.23% additional.
Health & Beauty
Amid health & beauty subcategories, Moisturizes witnessed the highest markdowns at 20.10%, followed by Make Up at 18.90%. The Moisturizer subcategory also offers highest additional discounts at 12.20%, followed closely by Sunscreen at 10.25% and Beard Care at 10.22%.
The Toothpaste subcategory has the lowest discounts, at 10.90%. The lower discounts on everyday essentials like this might indicate a steady demand or an attempt to maintain margins on frequently purchased items.
Most Health & Beauty subcategories fall in the 15-18% range for actual discounts and 8-10% range for additional discounts. Electric Toothbrush (16.90% actual, 9.91% additional) and Shampoo (16.50% actual, 8.78% additional) represent the middle of the pack. There were a few highly attractive deals though, such as the Philips Sonicare toothbrush retailing at $122.96 (down from $199.99), with a 39% discount.
Amazon also offered significant discounts on Open Box products (products that are returned, but unused, out of mint condition boxes) to Prime members.
Home & Furniture
This category saw the lowest discounts for this Prime Day event at 13.1%. Across subcategories, Rugs lead with the highest average discount at 21.50%, closely followed by Luggage at 20.90%. Amazon seems to be pushing decorative and organizational items (Rugs, Bookcases) more aggressively, possibly due to higher margins. Rugs also stood out as the subcategory with the highest additional discount of 11.54%.
Sofas have the lowest additional discount at 2.76%, followed by Dining Tables at 3.21%. Items like Cabinets (15.80% absolute, 6.66% additional) and Coffee Tables (14.40% absolute, 6.25% additional) represent the middle range of discounts.
Watch Out For More
As the holiday season approaches, it’s clear that the retail landscape continues to evolve. While Amazon remains a formidable force, there are opportunities for savvy competitors to carve out their niches and attract deal-hungry shoppers. By analyzing these trends and adjusting strategies accordingly, retailers can position themselves for success in the high-stakes world of summer sales events.
Stay tuned to our blog for more insights on how Amazon’s competitors reacted to Prime Day, and how leading brands across categories fared in terms of their pricing and their visibility during the sale event. Reach out to us today to learn more.
In 2025, both consumers and retailers continue to prioritize better health – albeit with evolving definitions and expectations.
The pandemic fundamentally transformed how consumers approach wellness, with this shift becoming entrenched in shopping behaviors years later. As shopping habits have permanently altered, retailers now face increased pressure to rapidly adapt their assortments with in-demand health and wellness products that enhance customer experience across various channels – online and offline.
Let’s explore how leading retailers are keeping consumers – and their own bottom lines – healthy by responding effectively to market trends to drive online sales and market share.
Health & Wellness Influence The Product Mix Across Categories
Consumption habits have changed dramatically since the onset of the pandemic. A McKinsey study shows that 82% and 73% of US, and UK consumers respectively now consider health & wellness a top priority. Typically shoppers adjust grocery shopping and meal planning at the start of the year, with many focusing on fresh, organic, and nutrient-rich foods.
The influential health and wellness mega-trend spans diverse retail channels, including grocery, pharmacy and mass. It extends across numerous categories like:
Food and beverage (natural, organic, vegan, plant-based food)
Health and personal care
Beauty
Cleaning products
Fitness equipment
Athleisure (apparel)
Consumer electronics like health wearables.
Today’s health movement is so powerful and compelling that retailers have revised their business strategies to better serve health-conscious consumers. For instance, drugstores are reinventing themselves as healthcare destinations, with CVS and Kroger expanding into personalized care delivery and value-based clinics to enhance their health offerings.
Major retailers like Amazon, Walmart, and Target report robust sales in health and wellness categories. For example, Walmart saw a 4.6% increase in comparable sales in early 2024, driven significantly by grocery, consumables, and health-related products.
New product categories are gaining traction:
Functional foods and beverages are seeing unprecedented growth, with Target launching over 2,000 wellness items in the category, including exclusive products priced under $10.
Personalized nutrition and mental health products are surging, including tailored dietary solutions and stress-reducing items.
Health wearables and wellness tech continue to rise in popularity, with over 150 new wellness tech items launched at Target this year, including innovative red-light therapy devices.
Transparency and sustainability certifications like organic, non-GMO, and vegan labels are increasingly driving purchasing decisions.
Clinically proven benefits offered by health & wellness products are gaining traction among Gen Z.
Retail’s Survival Of The Fittest Moves Online
As the omnichannel retail sector continues to grow, more shoppers now make purchase decisions within minutes using just a few clicks rather than physically visiting brick-and-mortar stores. In some cases, AI agents like Operator from Chat-GPT or Gemini (Google’s Chatbot) even make personalized, curated lists and reduce the time taken to make purchase decisions. Traditional retail paradigms are rapidly becoming obsolete as consumers grow savvier, more empowered, and better informed than ever before.
To stay competitive, more retailers are embracing AI-driven data insights to adjust their assortments to reflect consumer demand for health and wellness products.
According to industry experts, data insights have emerged as a critical retail strategy that continues to gain momentum. This is because retailers can no longer afford to guess how to approach their omnichannel strategy. They need the accuracy, clarity, and efficiency of data insights to guide their assortment and pricing decisions to outmaneuver competitors, maximize sales, and win market share as shopping evolves online.
Among its retail best practices, Bain & Company recommends retailers “lead with superior assortments that use a customer-centric lens to reduce complexity and increase space for the products customers love.” Insights can help retailers discover the optimal mix of national brands, private labels, limited-time offers, and value-added bundles.
“Lead with superior assortments … increase space for the products consumers love“ ~ Bain & Company
Determining the optimal mix of products also includes bestsellers and unique items that help retailers distinguish their offerings. Assortment insights help retail executives track competitors’ assortment changes and spot gaps in their own product assortment to adapt to emerging consumer trends and in-demand products.
Why Effective Assortment Planning Matters
Assortment planning sits at the heart of retail success, directly influencing profitability, customer satisfaction, and competitive differentiation. In today’s health-conscious market, getting your assortment right means:
Meeting Customer Expectations: Today’s health-conscious consumers expect relevant, high-quality products that match their wellness goals. A well-planned assortment signals that a retailer understands its customers’ evolving needs.
Optimizing Inventory Investment: Strategic assortment planning ensures capital is allocated to products with the highest return potential while minimizing investments in slow-moving items.
Creating Competitive Advantage: A distinctive assortment that includes popular health and wellness products alongside unique offerings helps retailers stand out in a crowded marketplace.
Reducing Lost Sales: Effective assortment planning minimizes the risk of stockouts on high-demand health and wellness items, preventing customers from shopping elsewhere.
Supporting Omnichannel Strategies: Well-executed assortment planning ensures consistency across physical and digital touchpoints, creating a seamless customer experience.
Improving Operational Efficiency: A thoughtfully curated assortment reduces complexity throughout the supply chain, from procurement to warehouse management to in-store operations.
As health and wellness continues to drive consumer spending, retailers who excel at assortment planning can capitalize on these trends more effectively than their competitors, turning market insights into tangible business results.
The synergy of AI and data analytics into retail assortment planning is changing how businesses approach inventory management. Retailers using AI-driven predictive analytics have achieved a 36% SKU reduction while increasing sales by 1-2%, showcasing the efficiency of data-driven approaches according to a McKinsey report.
Retailers face several challenges that can hinder strategic assortment planning:
Limited Understanding of Competition: Retailers struggle to gain comprehensive insights into their product assortments relative to competitors, often lacking visibility into their strengths and weaknesses across categories.
Data Overload: Assortment planning involves handling vast amounts of data, making it challenging for category managers to extract actionable insights without user-friendly tools and visualization.
Cross-Channel Consistency: With omnichannel retailing, ensuring consistency across physical stores, e-commerce, and other channels is complex. Misalignment can lead to customer dissatisfaction and loss of loyalty.
Adapting to Changing Market Trends: Identifying top-selling products and tracking consumer preferences is challenging. Balancing the right mix of products is crucial; without analytics, retailers risk lost sales or excess slow-moving inventory.
Scalability and Efficiency: As retailers expand into new markets or categories, scaling their assortment planning processes efficiently becomes a challenge. Legacy systems and manual methods often fail to support the agility needed for quick decision-making at scale.
DataWeave’s Assortment Analytics helps retailers address these challenges by providing a robust, easy-to-use platform that delivers actionable insights into product assortments and competitive positioning. With AI-driven, contextual insights and alerts, retailers can effortlessly identify high-demand, unique products, capitalize on catalog strengths, optimize pricing and promotions, improve stock availability, and refine assortments to maintain a competitive edge.
Beyond Data: Actionable Insights That Drive Results
DataWeave’s platform provides a comprehensive, insight-led view into assortments through several key dimensions:
Stock Insights: Monitor stock changes across retailers to stay updated on availability.
Category and Sub-Category Insights: Analyze assortment changes, identify newly introduced or discontinued categories, and track leading retailers in specific segments.
Brand Insights: Identify newly introduced, missing, or discontinued brands, as well as leading brands within chosen categories.
Product Insights: Identify bestsellers and evaluate their impact on your portfolio, analyzing pricing and promotions.
Personalized Recommendations: Receive suggestions tailored to your behavior and user profile to refine decision-making.
User-Configured Alerts: Stay informed with alerts designed to highlight significant changes or opportunities.
The platform addresses data overload by providing an intuitive, insight-driven view of your assortment. Category managers gain a comprehensive, bird’s-eye perspective of key changes within specified timeframes, allowing them to focus on what matters most.
Preparing for the Future of Retail Health
To avoid supply chain bottlenecks, inventory shortages, and out-of-stock scenarios, retailers are strategically using data insights to anticipate fluctuations in demand and proactively plan how to manage disruptions that could affect their assortments.
For variety that satisfies consumers’ diverse product needs, retailers are using data insights to determine whether to collaborate with nimble suppliers to promptly fill any gaps.
To further strengthen their assortments’ attractiveness, retailers are using AI-powered pricing analytics to offer the right product at the right price. These analytics help retailers know exactly how they compare to rivals’ pricing moves with relevant data so they can keep up with market fluctuations and stay competitive by earning consumer engagement, sales, and trust.
To Conclude…
Like nourishing habits that improve consumers’ health, data insights improve retailers’ e-commerce health. Advanced assortment and pricing analytics, powered by artificial intelligence, help retailers make better decisions faster to boost their agility, outmaneuver rivals, and fuel online growth.
In a retail landscape where consumer preferences for health and wellness continue to evolve rapidly, the retailers who thrive will be those who leverage data and AI to understand, anticipate, and meet these changing demands with the right products at the right time. Reach out to us to know more.
Cinco de Mayo is a vibrant celebration of Mexican-American and Hispanic heritage, marked by lively parades, festive tacos, and refreshing tequila across North America. For the service industry, brands, and retailers, this day offers a golden opportunity to roll out enticing promotions on beloved Mexican foods and beverages, drawing in large crowds and boosting sales.
Americans love to indulge in Mexican cuisine during Cinco de Mayo. Take avocados, for example: despite inflation, avocado sales soared to 52.3 million units this year, marking a 25% increase from last year, according to the Hass Avocado Board’s 2023 Holiday Report. Such festive events see a significant sales spike, largely driven by appealing discounts and special offers.
So, what discounts did retailers roll out this Cinco de Mayo?
At DataWeave, our cutting-edge data aggregation and analysis platform tracked and analyzed the prices and deals on Mexican food and alcohol products offered by leading retailers. Our in-depth analysis sheds light on their pricing competitiveness during Cinco de Mayo, revealing how pricing strategies differed across various subcategories and brands.
We conducted a similar analysis in 2022, allowing us to compare the prices of identical products this year versus last year. This comparison helps us understand the impact of inflation over the past two years on the prices offered today.
Our Methodology
For our analysis, we monitored the average discounts offered by major US retailers on over 2,000 food and beverage products during Cinco de Mayo, as well as in the days leading up to the event. Many retailers kick off their Cinco de Mayo promotions a week before, so we included the entire week leading up to May 5th in our analysis.
To truly demonstrate the value of Cinco de Mayo for shoppers, we concentrated on price reductions and additional discounts during the event. By comparing these with regular day discounts, we were able to highlight the genuine savings and benefits that Cinco de Mayo promotions offer to budget-conscious consumers.
Our Findings
Safeway led the pack with the highest average additional discount of 4.91%, covering 38.6% of their food inventory for Cinco de Mayo. Total Wine & More followed closely, offering an average discount of 3.46% across 70.8% of its tequila, whiskey, mezcal, and other spirit products during the Cinco de Mayo week.
In contrast, Target provided minimal additional discounts, averaging just 0.8% over a small fraction (11.6%) of its SKUs. Similarly, Kroger’s additional discounts were also 0.8%, but they were spread across over 60% of its tracked products. Walmart (1.4%) and Amazon Fresh (1.2%) offered relatively conservative discounts during the sale period.
During Cinco de Mayo, various brands rolled out attractive discounts to entice shoppers. Among beverage brands, The American Plains vodka led the way with the highest average discount of 20.80%. Coffee brands also joined the festivities with significant discounts: Death Wish Coffee at 14.30%, Dunkin’ at 11.10%, and Starbucks at 5.70%. Notably, Dunkin’ and Death Wish Coffee introduced complimentary beverages such as whiskey barrel-aged coffee and spiked coffee products to celebrate the event.
In the wine category, Erath stood out with a 10% additional discount. However, brands like Jose Cuervo and Franzia offered more modest discounts of 0.70% and 1.80%, respectively.
Food brands associated with traditional Mexican ingredients or products, such as tortillas, salsas, and spices, provided higher discounts compared to mainstream snack brands. For instance, McCormick (25%), El Monterey (13.3%), and La Tortilla Factory (16.7%)—known for ready-to-eat frozen foods, seasonings, and condiments—delivered the highest discounts. Other notable discounts included Jose Ole (12.5%), a frozen food brand, and Yucatan (8.3%), known for its guacamole.
Safeway’s private label brand, Signature Select, offered a 5.20% discount. Additionally, Safeway provided deep discounts on brands like Pace, Herdez, and Taco Bell, indicating an aggressive discounting strategy. In contrast, brands closely associated with Mexican or Tex-Mex cuisine, such as Old El Paso, Mission, Rosarita, and La Banderita, offered relatively modest discounts ranging from 0.5% to 3.3%.
The discount patterns varied between alcohol and food categories, with food brands generally offering higher discounts. This trend may be attributed to pricing being regulated in the alcohol industry. These differing discount levels highlight how brands navigated the balance between driving sales and maintaining profit margins during Cinco de Mayo, particularly in the context of inflation affecting costs.
Impact of Inflation on Cinco de Mayo Prices (2024 vs 2022)
To gauge the impact of inflation on popular Cinco de Mayo products, we analyzed the average prices at Walmart and Target between 2022 and 2024. These two retailers were chosen due to their prominence in the retail sector and the robustness of our sample data.
At Walmart, the Tex Mex category saw the highest average price increase, rising by 22.51%. Other notable subcategories with significant price hikes include Condiments (23.21%), Vegetables/Packaged Vegetables (21.22%), and Lasagne (14.10%). Categories like Dips & Spreads (13.77%), Pantry Staples (14.92%), and Salsa & Dips (8.23%) experienced relatively lower increases.
At Target, the Snacks subcategory had the steepest average price rise at 27.94%, followed by Meal Essentials (16.07%) and Deli Pre-Pack (8.82%). Categories such as Dairy (0.51%), Frozen Meals/Sides (7.11%), and Adult Beverages (7.41%) saw smaller price increases.
Brands associated with traditional Mexican or Tex-Mex cuisine faced higher price hikes. Examples include Old El Paso (24.59% at Walmart, 8.70% at Target), Tostitos (35.44% at Walmart, 11.41% at Target), Ortega (30.59% at Walmart, 19.69% at Target), and Rosarita (14.39% at Walmart).
In contrast, private label or store brands generally experienced lower price increases compared to national brands. For instance, Good & Gather (Target’s private label) saw a 9.55% increase, while Market Pantry (Walmart’s private label) had a 17.27% rise. This trend is understandable as retailers have more control over their costs with private label brands.
The data clearly indicates that both Walmart and Target have significantly raised prices across various categories and brands, reflecting the broader inflationary environment where the cost of goods and services has been steadily climbing.
Interestingly, we observed higher price increases at Walmart compared to Target. Although Walmart is renowned for its consumer-friendly pricing strategies, it too had to elevate grocery prices post-2022 to combat inflationary pressures. As consumers become more cost-conscious and reduce spending on discretionary items, Walmart and other retailers are now cutting prices across categories to align with shifting consumer behaviors.
Mastering Pricing Strategies During Sale Events
Our pricing analysis for Cinco de Mayo reveals compelling insights into the dynamics of retailer landscapes in the US. It highlights the enduring relevance of private label brands, even amidst fluctuating demand, showing the emergence of local, national, and small players vying for market share.
As retailers navigate inflationary pressures and evolving consumer behaviors, understanding these pricing dynamics becomes crucial for optimizing strategies and bolstering market competitiveness. This analysis offers actionable intelligence for retailers seeking to navigate the intricate terrain of sale event promotions while addressing shifting consumer preferences and economic challenges.
Access to reliable and timely pricing data equips retailers and brands with the tools needed to make informed decisions and drive profitable growth in an increasingly competitive environment. To learn more and gain guidance, reach out to us to speak to a DataWeave expert today!
On Black Friday this year, Health & Beauty brands saw a significant increase with a 13% jump in foot traffic, according to a report by RetailNext. Despite caution from various sources, higher prices for everyday goods, and high interest rates, consumers chose to spend big this cyber week.
So what kind of deals did top retailers and brands offer in the Health & Beauty category this BFCM? At DataWeave, we harnessed the power of our proprietary data aggregation and analysis platform to track and analyze the prices and deals of Health & Beauty products across prominent retailers to uncover unique insights into their price competitiveness this BFCM, as well as understand how pricing strategies varied across diverse subcategories and brands.
For this analysis, we tracked the average discounts among leading US retailers in the Health & Beauty category during the Thanksgiving weekend sale, including Black Friday and Cyber Monday. We noticed prices and discounts didn’t change significantly over the course of the weekend, and hence the average prices of products between the 24th and 27th of November are being reported. Our sample was chosen to encompass the top 500 ranked products in each product subcategory across leading retailers during the sale.
Subcategories reported on: Shampoo, Toothpaste, Conditioner, Sunscreen, Makeup, Electric Toothbrush, Beard Care, Moisturizer
Timeline of analysis: 24 to 27 November 2023
Our Key Findings
Average Discounts Across Retailers
Amazon leads the pack with a huge margin, offering an average discount of 31.9%, covering 62% of its products analyzed. Target follows an 18.8% average discount across only 5% of its analyzed assortment. The other retailers aren’t even close.
Ulta Beauty was the next in line, providing a 9.2% average discount followed by Walmart with a 6.8% average discount. Sephora, known for its premium beauty offerings, adopted a more conservative approach with a 3.5% average discount, targeting only 9% of its top products
Across retailers, it is clear that Amazon led the charge by far this cyber week, with the other retailers choosing to markdown prices conservatively in the Health & Beauty category.
Average Discounts: Subcategories
Amazon offered high discounts on lower priced subcategories like Toothpaste (49.4%), Sunscreen (46.3%), Moisturizers (38.5%), and Conditioners (37.5%), highlighting its focus on products with high demand that consumers would look to stock up on. Ulta Beauty also focused its discounts on Toothpaste (15.6%), Moisturizers (14.9%), and Conditioners (12.6%), targeting skincare and grooming.
Sephora, meanwhile, offered the most attractive deals on the Makeup subcategory at 5.3% across 12.67% of its analyzed assortment, banking on the demand generated due to the brand’s popularity in this subcategory.
Target prioritized discounts on Toothpaste (22.5%), Shampoo (21.6%), and Moisturizers (18.9%). Walmart too offered significant discounts on Shampoo (21.6%) and Toothpaste (22.5%).
Retailers prioritized staple subcategories like Toothpaste and Moisturizer with substantial discounts during this Black Friday Cyber Monday, ensuring a broad consumer appeal. In contrast, discretionary items like Makeup may be less motivated by discounts alone, and hence saw lower discounts during the sale.
Average Discounts: Brands
Brands offered the most attractive deals on Amazon, with OGX leading the pack at 58.4% average discount. Neutrogena and Colgate followed with an average discount of 50.4% and 44%. This mirror’s Amazon’s subcategory focus on shampoos, conditioners, and toothpastes.
Other instances of brands offering attractive deals across retailers include Belif (27.9%) and Anastasia Beverly Hills (17.6%) on Sephora, Johnson’s (20%) and Philips Sonicare (18.8%) on Target, and Olay (12.2%) and Colgate (10.6%) on Walmart.
Ulta Beauty hosted several attractive deals by specific brands, including Moon (30.7%), Joico (24%), and Clinique (22.3%).
Share of Search For Health & Beauty Brands Across Subcategories
Our Share of Search analysis illuminates the strategic moves made by brands to enhance their visibility, playing a crucial role in influencing consumer choices during Black Friday and Cyber Monday.
Among some of the leading brands, Head & Shoulders and Oral-B increased their Share of Search by 2.3% and 1% respectively, reflecting a successful strategy to boost brand visibility during the Black Friday and Cyber Monday shopping events. On the other hand, L’Oreal Paris, Colgate, and Neutrogena faced marginal decreases in Share of Search.
Overall, since the difference in Share of Search values did not change dramatically, the visibility levels of leading brands across key subcategories remained consistent during the Thanksgiving weekend.
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For deeper insights on pricing and discounting trends across a diverse range of shopping categories during Black Friday and Cyber Monday, check out our blog!
To learn more about our AI-powered Pricing Intelligence and Digital Shelf Analytics platform, contact us today!
Insider Intelligence‘s forecast of a 4.5% growth in US Holiday Sales this year has been validated by the sustained robust spending observed during Black Friday and Cyber Monday. Despite multiple challenges impacting consumer spending, such as escalating prices of everyday products and elevated interest rates, shoppers continued to spend significantly, aligning with these earlier predictions.
However, in response to these projections, retailers strategically adjusted their approach. Our analysis indicates substantial discounts prevalent in the Consumer Electronics and Home & Furniture segments during Cyber Week. Prominent retailers specializing in Home & Furniture, such as Wayfair, Overstock, and Home Depot, notably led the charge in offering attractive discounts.
At DataWeave, we harnessed the power of our proprietary data aggregation and analysis platform to track and analyze the prices and deals of home & furniture products across prominent retailers to uncover unique insights into their price competitiveness this BFCM, as well as understand how pricing strategies varied across diverse subcategories and brands.
We’ve also recently published our analysis of the Consumer Electronics and Apparel categories this Black Friday and Cyber Monday.
Our Methodology
For this analysis, we tracked the discounts offered by leading US retailers in the Home & Furniture category during the Thanksgiving weekend sale, including Black Friday and Cyber Monday. We noticed prices and discounts didn’t change significantly over the course of the weekend, and hence the average prices of products between the 24th and 27th of November are being reported. Our sample was chosen to encompass the top 500 ranked products in each product subcategory across leading retailers during the sale.
Sample size: 44,716 SKUs
Retailers tracked: Amazon, Walmart, Target, Best Buy, Overstock, Wayfair, Home Depot
Subcategories reported on: Dishwasher, Washer/Dryer, Mattresses, Beds, Dining Tables, Entertainment Units, Rugs, Luggage, Bookcases, Cabinets, Sofas, Coffee Tables
Timeline of analysis: 24 to 27 November 2023
Our Key Findings
Discounts Across Retailers
Wayfair led the pack with the highest average discount of 27.5%, covering an impressive 88% of its Home & Furniture inventory. This bold strategy positions Wayfair as a go-to destination for consumers seeking substantial savings on high-quality Home & Furniture items during Black Friday and Cyber Monday.
Home Depot offered an average discount of 17.5%, covering a substantial 69% of the products analyzed, choosing to cash in on the Cyber Week madness. Overstock followed next with an average discount of 16.6%.
Interestingly, Home & Furniture happens to be one of the few categories in which Amazon did not offer the highest discount among the analyzed retailers, choosing a moderate average discount of 13.8%.
Best Buy also maintained a competitive stance in the category, providing an average discount of 12.8% across 58% of their assortment. Target adopted a conservative markdown strategy, offering a relatively low average discount of 6.5%.
In summary, the Home & Furniture category exhibited a diverse range of discounting strategies among retailers, reflecting a balance between competitiveness and profit margins. Consumers could have chosen from a spectrum of discounts based on their preferences and budget considerations during Black Friday and Cyber Monday.
Average Discounts: Subcategories
Among subcategories, Amazon offered a moderate 8.3% average discount on 32.9% of its products in this Dishwasher category, while Best Buy took a more aggressive stance with a 14.7% average discount covering 55.9% of its products.
Home Depot emerged as a standout player in the Washer/Dryer category, providing a substantial 21.3% discount on 78.4% of its analyzed inventory. Best Buy closely followed with a 15.1% average discount targeting 67.6% of its products.
Wayfair grabbed attention with a generous 36.9% average discount on Mattresses, covering almost all (99%) of its analyzed products. In addition, Wafair led the discount war in Beds, Dining Tables, Cabinets, Sofas, Coffee Tables, and Entertainment Units. Overstock took an aggressive pricing stance on Rugs, offering a substantial 52.3% average discount, covering 100% of its Rugs inventory.
Average Discounts: Brands
Among brands, Signature Design by Ashley maintained a consistent presence with substantial discounts on both Best Buy (25.24%) and Overstock (16.19%). This could be indicative of the brand’s commitment to appealing to a diverse customer base through varied retail channels. Costway emerges as a standout brand offering exceptionally high discounts at both Target (61.6%) and Walmart (51.7%).
Home Decorators Collection, Home Depot’s in-house brand, offered a significant 30.9% discount at Home Depot. High-margin private label brands like these afford retailers the opportunity to offer markdowns while retaining significant margins.
Strategic positioning on specific platforms, as seen with Alwyn Home on Wayfair and Noble House at Home Depot, suggests brands tailor their approach to the strengths and customer demographics of each retailer. The data suggests a nuanced interplay between brand positioning, discount strategies, and the perceived value offered.
Share of Search For Home & Furniture Brands
The Share of Search data for the Home & Furniture category unveils intriguing insights into brand visibility and performance during the Black Friday and Cyber Monday events. In this competitive landscape, where consumer decisions are influenced not only by discounts but also by brand visibility, the dynamics of Share of Search become pivotal.
Samsung strategically increased its Share of Search during the sale, showcasing a 1.2% improvement. This suggests a deliberate effort to reinforce brand visibility and capture the attention of potential buyers actively searching for Home & Furniture products, in this case, Washer/Dryers and Dishwashers.
Bosch too experienced a notable surge in Share of Search by 1.1%. LG, meanwhile, maintained a consistent Share of Search, with a marginal decrease of 0.1%. American Tourister experienced a modest increase in Share of Search by 0.4%.
Like in the other categories analyzed, the dynamics of Share of Search in the Home & Furniture category reflect brand strategies aimed at not only offering discounts but also ensuring heightened visibility during the critical Black Friday and Cyber Monday shopping events. Positive shifts indicate effective marketing efforts, while stable performers demonstrate a resilient brand presence in a competitive online marketplace.
To explore how our insights can help retailers and brands boost their pricing strategies during sale events, reach out to us today!
For more in-depth analyses and trends across various shopping categories, stay tuned to our blog.
As the highly anticipated shopping season approached, industry analysts, including Deloitte, had forewarned consumer spending caution owing to persistent inflationary pressures tightening budgets. Despite these concerns, the holiday spirit was buoyed by sensational deals that delighted bargain-hunting shoppers.
According to the National Retail Federation (NRF), over 200 million consumers participated in both in-store and online shopping activities over the Thanksgiving weekend. This marked an almost 2% uptick from the previous year, surpassing the NRF’s initial estimates of 182 million and showcasing a robust start to the holiday shopping season.
So what was all the hype about this Black Friday and Cyber Monday? How did top retailers react to reports of possibly decreased consumer spending? At DataWeave, we harnessed the power of our proprietary data aggregation and analysis platform to track and analyze the prices and deals of products across prominent retailers and categories to uncover unique insights into their price competitiveness this BFCM, as well as understand how pricing strategies varied across diverse subcategories and brands.
In this article, we focus on the pricing and discounting strategies of Amazon, Walmart, and Target in the Apparel category.
Stay tuned to our blog for insights on other shopping categories like Home & Furniture, and Health & Beauty!
Our Methodology
For this analysis, we tracked the average discounts of apparel products among leading US retailers during the Thanksgiving weekend sale, including Black Friday and Cyber Monday. We noticed prices and discounts didn’t change significantly over the course of the weekend, and hence the average prices of products between the 24th and 27th of November are being reported. Our sample was chosen to encompass the top 500 ranked products in each product subcategory across during the sale.
Amazon offered the most attractive deals, showcasing an average discount of 19.5%, applying to a substantial 61% of their apparel inventory.
Trailing closely behind was Target, offering an average discount of 14.8% across 52% of the products analyzed. Walmart, however, took a more conservative approach, providing an average discount of 8.5%, applicable to 29% of its products.
The contrast in discounting strategies highlights the diverse tactics employed by retailers to entice Black Friday and Cyber Monday shoppers within the Apparel category. Amazon remains the forerunner, balancing competitive discounts with a significant coverage of discounted items.
Target follows suit with a competitive stance, while Walmart opts for a more reserved markdown approach, given that the retailer tends to carry a large number of products in the affordable price ranges.
Average Discounts: Subcategories
Examining the Black Friday and Cyber Monday discount landscape within the Apparel category reveals intriguing patterns among major retailers. Amazon led the charge, boasting an impressive 24.9% average discount on Women’s Tops, covering a substantial 76.5% of its products. In the same subcategory, Target competed fiercely with a 25.1% average discount, covering 87.5% of its products. Walmart, taking a measured approach, presented a 14.6% average discount across 45.1% of its Women’s Tops inventory.
Notably, Men’s Swimwear at Target has no discounts. Meanwhile, Amazon remained aggressive across various subcategories, particularly in Women’s Shoes and Women’s Tops, aiming to capture a significant market share through both competitive pricing and a broad coverage of discounted items.
Average Discounts: Brands
Across brands, Tommy Hilfiger and Jockey took the lead on Amazon with an enticing average discount of 28.3% and 24.6% respectively, appealing to savvy shoppers. Calvin Klein followed closely with a 17.3% discount, offering a balance of style and affordability.
In Walmart, Crocs stood out with a 39.9% average discount, followed by Reebok (15.7%) and Hanes (14.9%) Xhilaration, Target’s in-house brand, stole the spotlight on the retailer platform with an impressive 50% average discount. Reebok (32.3%) and Levi’s (22.9%) maintained competitive discounts, appealing to diverse tastes.
Our analysis sheds light on the dynamic landscape of apparel discounts, showcasing how brands adopt varying pricing strategies to position themselves competitively for Black Friday and Cyber Monday shoppers.
Share of Search For Apparel Brands Across Subcategories
The dynamics of Black Friday and Cyber Monday extend beyond price reductions, with brands strategically vying for increased visibility through Share of Search metrics. This metric signifies a brand’s prominence among the top 20 ranked products in a given subcategory, offering valuable insights into their online marketplace visibility.
Among the standout performers in the Apparel category, Jockey experienced a significant surge in Share of Search, leaping from 1.70% before the event to an impressive 13.30% during the Black Friday and Cyber Monday sales. Speedo, in the Women’s Swimwear subcategory, demonstrated a substantial increase from 4.40% to 13.30%, solidifying its presence and gaining an 8.90% boost in Share of Search.
Tommy Hilfiger and Adidas also exhibited notable gains in Share of Search, increasing by 5.30% and 5.60%, respectively. However, some brands experienced a slight dip, with Speedo in the Men’s Swimwear subcategory seeing a 2.50% dip in their search visibility, and Reebok in Men’s Shoes witnessing a 3.3% decrease.
These fluctuations highlight the dynamic nature of brand strategies during Black Friday and Cyber Monday in the Apparel category, where gaining visibility also proves to be crucial alongside offering competitive discounts.
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For a deeper dive into the world of competitive pricing intelligence and to explore how our solutions can benefit apparel retailers and brands, reach out to us today!
Stay tuned to our blog for forthcoming analyses on pricing and discounting trends across a spectrum of shopping categories, as we continue to unravel the intricacies of consumer behavior and market dynamics.
As Black Friday and Cyber Monday unfolded across the globe, there was a noticeable subdued atmosphere compared to previous years. TD Cowen brokerage adjusted its forecast for US holiday spending, revising it down from an initial 4-5% growth to a more conservative estimate of 2-3%.
Compounded by persistent inflation and elevated interest rates, many consumers find themselves financially strained, leading to the projection of the slowest growth in US holiday spending in five years.
In this context, it would be relevant to investigate whether this restrained reaction from consumers had an influence on the extent of attractive deals and discounts provided by top retailers and brands during the sale event.
At DataWeave, we harnessed the power of our proprietary data aggregation and analysis platform to track and analyze the prices and deals of consumer electronics products across prominent retailers to uncover unique insights into their price competitiveness this BFCM, as well as understand how pricing strategies varied across diverse subcategories and brands.
Keep an eye on our blog for insights on other shopping categories like Apparel, Home & Furniture, and Health & Beauty!
Our Methodology
For this analysis, we tracked the average discounts among leading US electronics retailers during the Thanksgiving weekend sale, including Black Friday and Cyber Monday. We noticed prices and discounts didn’t change significantly over the course of the weekend, and hence the average prices of products between the 24th and 27th of November are being reported. Our sample was chosen to encompass the top 500 ranked products in each product subcategory across leading retailers during the sale.
Sample size: 23,505 SKUs
Retailers tracked: Amazon, Walmart, Target, Best Buy
The observed Black Friday and Cyber Monday discount strategies reveal a distinct competitive landscape among major retailers. Amazon emerged as the frontrunner, offering the highest average discounts at 23.30%, spanning a significant 74% of their consumer electronics inventory. Best Buy closely followed, with an average discount of 19.40% across 76% of their products.
On the other hand, Target and Walmart adopted a more conservative stance, providing lower average discounts at 14.8% and 12%, respectively, with Target discounting 51% of its products and Walmart discounting 41%. This variation in discounting strategies highlights the diverse approaches retailers take to attract and retain Black Friday and Cyber Monday shoppers, balancing competitiveness with profit margins.
Average Discounts: Subcategories
In the Headphones subcategory, Amazon stands out with a substantial 31.40% average discount, targeting 84.69% of SKUs, showcasing an aggressive discounting strategy. Best Buy follows closely, demonstrating competitive pricing with a 21.80% average discount on 67.03% of products.
Meanwhile, in TVs, Best Buy offered a significant 17.9% average discount across 89% of its products, signaling a targeted effort to capture a broad market share in this subcategory.
In the Laptop subcategory, Target was highly conservative, with only a 4.1% average discount covering 14.3% of its products, while Walmart positioned itself with a moderate 9.5% average discount, targeting 39.8% of its inventory.
Among Smartphones, Amazon (14.7%) was third to Best Buy and Target, which offered average discounts of 20.5% and 18.1%, respectively. Walmart, with an average discount of only 9.9% in the subcategory opted for a relatively muted approach.
Average Discounts: Brands
The discount strategies across top electronics brands during Black Friday unveil distinct approaches. Samsung emerges as a focal point across Amazon, Best Buy, Walmart, and Target. The brand was most attractively priced on Best Buy, with an average discount of 25.3%, followed by Target (18.3%) and Amazon (17.9%).
Apple’s discounts were quite consistent across Amazon (17.6%), Best Buy (16.1%), and Target (17.8%), with the exception of Walmart (8.1%). JBL, interestingly, opted to discount very heavily on Best Buy, at an average of 38.8%, resulting in several attractive deals for shoppers on the website. Sony, too, offered impressive discounts at over 23% on Amazon and Best Buy, followed by 16% on Walmart. On Amazon, Amazon Renewed (13.9%) was among the most aggressively discounted products, highlighting an effort to further appeal to cost-conscious consumers.
Overall, our analysis throws light on the nuanced strategies employed by leading brands on Amazon, Best Buy, Walmart, and Target, reflecting a delicate interplay between brand positioning, pricing competitiveness, and customer appeal.
Share of Search For Consumer Electronics Brands Across Subcategories
The Share of Search data reflects intriguing shifts in brand strategies during the Black Friday and Cyber Monday events. During sale events, brands looking to entice shoppers don’t rely only on price but also on search visibility to help drive awareness and conversion. Share of Search is defined as the share of a brand’s products among the top 20 ranked products in a subcategory, thereby providing insight into a brand’s visibility on online marketplaces.
Some of the brands that improved their Share of Search the most include LG, Skullcandy, Asus, JBL, and Samsung. On the other hand, prominent brands like Sony and Apple actually lost ground on this metric by 0.4% and 2% respectively.
At DataWeave, our commitment to empowering retailers and brands with actionable competitive and digital shelf insights remains unwavering. Our AI-powered platform provides a comprehensive view of market dynamics for our customers, enabling informed decision-making. As a partner in your journey, we offer tailored solutions to enhance your competitive edge, drive sales, and elevate your brand presence. To find out more about our solution, reach out to us today!
To learn more about pricing and discounting trends during Black Friday and Cyber Monday across various other shopping categories, stay tuned to our blog!
This year’s back-to-school shopping season has presented a considerable challenge for inflation-weary parents in the US. Despite chatter about alleviating inflation rates, the reality of rising prices tells a different story.
As families hunt for school supplies, apparel, and other essential items for the academic year, the financial strain remains palpable. Experts note that elevated prices coupled with extensive shopping lists have compelled many parents to be more discerning about their purchases, expenditure thresholds, and preferred shopping venues. Essentially, shoppers are looking for more value for their money with every purchase. According to the National Retail Federation’s 2023 projection, this back-to-school season is poised to be the most financially demanding one to date. The forecast anticipates total spending exceeding $135 billion, marking an increase of over $24 billion compared to the previous year.
At DataWeave, we continually monitor and analyze pricing activity among retailers across popular shopping categories. Our recent study delved into the pricing trends in the back-to-school category, which includes backpacks, fundamental school supplies, binders, planners, writing instruments, and more. The aim was to understand how the costs of back-to-school essentials have shifted in 2023 in comparison to 2022.
Pricing of Back-to-School Products in 2023
Our analysis, spanning 1200 products across major retailers such as Amazon, Walmart, Kroger, and Target reveals an average price surge of 9.8% in 2023 compared to the previous year.
This upward pricing trend can be attributed to retailers’ strategic efforts to guarantee product availability and uphold quality during a period of heightened demand. As the back-to-school season sparks a surge in shopping activity, retailers like Kroger, Amazon, and Walmart are likely adjusting prices strategically to align with the expenses incurred in securing adequate supplies, managing logistics, and meeting operational demands.
Kroger led the way with a 12.1% price hike, the most significant among the scrutinized retailers. It was followed by Amazon with an average increase of 10.5% and Target with 7.8%. Walmart remains the outlier, with the smallest price increases for back-to-school products in 2023.
Pricing across Categories and Subcategories
Among the various categories examined, backpacks have experienced the most pronounced escalation, with prices soaring by a substantial 25%. Within the top 10 highest priced backpacks we looked at, the most substantial price hikes were observed for brands like The North Face (44%) and Fjallraven (33%).
The Office Organization category also witnessed a significant price surge of 16.8%, attributed to subcategories like File Folders and Desk Accessories, which saw respective price hikes of 31.3% and 25.2%.
Categories like Memo Boards & Supplies (14.3%), Binders (12.5%), and Themebooks & Portfolios (12.4%) have likewise encountered notable price hikes. On the other end of the spectrum, Planners and Journals saw a modest rise of 4.4%, while Mailing and Shipping Supplies and Office Machine Accessories experienced comparatively lower price increases at 7% each.
Interestingly, while items like Journals and Writing Instruments maintain popularity year-round, Backpacks and Memo Boards are particularly sought after during the back-to-school season, contributing to more substantial price hikes in these categories.
On the other hand, consumers are consistently on the lookout for cost savings and deals from retailers, especially as they deal with inflationary pressures. In response, Kroger, Target, and Walmart have introduced back-to-school savings initiatives. For instance, Kroger is offering more than 250 items for less than $3 and some items for just $1, encompassing essentials such as paper, pencils, and glue sticks. Lower price increases across categories like journals and writing essentials could be attributed to these initiatives.
Brands with the Highest Price Increases across Categories
Across various back-to-school categories, some brands stand out with significant price increases. For instance, in the Office Organization category, Ubrands leads the pack with a substantial 38.30% surge, followed by Pendaflex at 30.80%. Meanwhile the Backpacks category sees Champion and Adidas recording significant price jumps of 29.6% and 23.6%, respectively.
Ubrands and Pentel from Basic School and Office Supplies Category also record high price increases at 22.70%, followed by Carolinapd from the Themebooks & Portfolios Category at 21.08%. 3M in Mailing in Shipping Supplies shows the lowest price increase at 6.80%.
Interestingly, the ever popular Writing Instruments category showcases BIC at the forefront, exhibiting the most notable price escalation of 13.2%. Expo trails closely at 11.6%, while Uniball demonstrates an 11.4% increase. Even Sharpie, a beloved writing brand, displays a modest price uptick of 9.3%.
The average price increments seen across brands mirror the overarching trend of increased costs throughout back-to-school categories.
Navigating the Competitive Pricing Landscape During the Back-To -School Season
Given the challenging pricing landscape during the back-to-school season, retailers would be wise to provide lower-cost alternatives alongside popular brand names. This allows parents to easily make substitutions while adhering to a school supplies list.
With our competitive pricing intelligence solution, retailers can confidently analyze and monitor their prices relative to competition, ensuring they maintain a leadership position in pricing within their desired set of products, while posturing for margins with other products.
Amazon’s India Prime Day 2023 shattered previous records with a peak of 22,190 orders received in a minute. An important aspect of Amazon’s India Prime Day was the benefits it offers to Prime Members. Thousands of sellers, brands, and bank partners collaborated to help Prime members save a staggering sum of over Rs. 300 Crores. The 2 day (July 15-16) event even witnessed strong growth in Prime membership, with 14% more members shopping than last year’s Prime Day event. 45,000+ new products were launched by over 400+ top Indian and global brands.
However, our analysis reveals that Amazon was able to make a huge splash despite adopting a relatively modest discounting strategy for the event.
Pricing and Discounts on Prime Day 2023
While Prime Day is Amazon’s showstopper, bringing huge benefits to partner brands and sellers, it’s interesting to also see how Flipkart responded to such a massive sale by its biggest competitor. Therefore, we leveraged our proprietary data aggregation and analysis platform to analyze the prices and discounts of Amazon and Flipkart across key product categories – Apparel, Home & Furniture, Consumer Electronics, and Health & Beauty – during Prime Day.
Since products on Amazon and other eCommerce websites are often sold at discounts even on normal days not linked to a sale event, we delved into the real value that Prime Day offers to shoppers by focusing on price reductions or additional discounts during the sale compared to the week before. As a result, our approach highlights the genuine benefits of the event for shoppers who count on lower prices during the sale.
Research Methodology
For our analysis, we tracked the prices of a large number of products across Amazon and Flipkart during Prime Day as well as the week prior to the event. The details of our sample are mentioned below:
Number of SKUs: 85,000+
Retailers: Amazon, Flipkart
Categories: Apparel, Home & Furniture, Consumer Electronics, Health & Beauty
Pre-event Analysis:10-14 July 2023
Prime Day Analysis: 15-16 July 2023
Our Findings
Based on our analysis, Prime Day showcased relatively higher price reductions in the Health and Beauty category, offering an average additional discount of 5.3%. In comparison, the Apparel category had lower discounts at 4.90%, followed by the Home & Furniture category at 2.50% during the sale event.
The Consumer Electronics category, known for attractive prices during sale events, featured only 0.9% price reductions. This is due to the fact that the category was already being sold at a very high average discount of around 44.8% the week prior to Prime Day.
Below, we delve deeper into our analysis of each category to better understand how price reductions were distributed across key subcategories on Amazon. We also report on the degree to which Flipkart responded to Amazon’s pricing actions during the event.
Apparel
As Amazon grappled with heightened costs and reduced profit margins in apparel (like most other retailers), its average discount before Prime Day was already at 36.5%. Then, on Prime Day, Amazon’s apparel deals were tempered at around 4.9% average price reduction across 43.7% of its assortment.
Flipkart, on the other hand, offered only a modest additional discount of 1.8% across 17.7% of its Apparel assortment. It’s clear that while Flipkart took steps to compete against Amazon in this category, it was done to a lower extent on fewer products than Amazon.
Across all the apparel subcategories we analyzed, Men’s Shoes (11.6%), Women’s Shoes (9.5%), and Men’s Shirts (8.7%) were among the ones with the highest price reductions. On the other hand, Men’s and Women’s Swimwear (2.3%), Women’s Innerwear (2.9%), and Women’s Athleisure (3.3%) had conservative markdowns.
Pricing choices within different subcategories likely stemmed from a range of factors, such as inventory quantities, trends in demand, and the aim to harmonize competitive deals with the maintenance of viable profit margins. These decisions reflect Amazon’s attempt to cater to a consumer base that is particularly conscious of pricing.
Across all apparel subcategories, leading brands that offered the highest markdowns were Sweet Dreams (65.5%), Ketch (55.1%), Clarks (44.9%), and Kibo (38.4%). Meanwhile, Reebok and Adidas offered significant additional discounts at 26.3% and 24.9%, respectively, as well.
For brands, however, reducing prices is just one approach to entice shoppers. They must also guarantee their prominent presence and easy discoverability within Amazon’s search results. This significantly amplifies their potential to generate higher clicks and conversions. In our analysis, we monitored brands’ Share of Search across various frequently used search terms in addition to the discounts they provided. The Share of Search denotes the portion of a brand’s products within the top 20 search results for a specific search query.
Our data indicates that certain brands gained ground in their discoverability during Prime Day, while others fell behind. Van Heusen in Women’s Athleisure (30%), Campus in Men’s Shoes (50%), and Rovar’s (30%) in Women’s Swimwear among others, improved their Share of Search by significant levels during Prime Day.
On the other hand, brands like Sparkx in Men’s Shoes, Xyxx in Men’s Innerwear, WomanLikeU in Women’s Swimwear, and Adidas in Women’s Shoes lost around 40%-80% in their Share of Search during the event. This is likely to have impacted their sales volumes adversely.
Home & Furniture
The Home & Furniture industry faced challenges of reduced demand and overstocked inventory over the past year. Therefore, even before Prime Day, discounts offered in this category on Amazon averaged a staggering 45.3%. Consequently, on Amazon Prime Day, additional discounts averaged only 2.5% on Amazon, offered across 33.3% of its assortment. Flipkart opted, in effect, not to compete with Amazon in this category, offering a negligible additional discount of 0.8% across 14.70% of its assortment.
Of all the Home & Furniture subcategories we analyzed, Luggage (5.1%), Beds (3.9%), and Coffee Tables (3.1%) had high price reductions, while Rugs (0.6%), Bookcases (1.5%), and Washer/Dryers (1.2%) had lower markdowns. This highlights the difference in consumer preferences across geographies, with rugs being more discretionary in India but staple in the US.
The Home & Furniture category is not known for its brand loyalty among shoppers. Therefore, brands often rely on attractive pricing to gain shopper interest. This Prime Day, brands that offered the highest markdowns in this category include It Luggage (40%), Couch Culture (25.8%), Story@Home (23.3%), and Verage (21.2%).
In terms of Share of Search, Wudparadise in Entertainment Units gained the highest (50%). Solimo (an Amazon Brand) in Beds (40%), Sofas (30%), and Coffee Tables (10%) gained significant ground in its respective categories too. In contrast, About Space in Bookcases (-60%), Anika in Entertainment Units (-40%), and Sleepyhead in Mattresses (-40%) lost out on their discoverability in their respective categories during the event.
To gain a competitive edge during sale events like Prime Day, brands need to monitor their Share of Search closely, especially in categories like Home & Furniture with low brand loyalty.
Consumer Electronics
This Prime Day, five smartphones got sold every second with 70% of the demand coming from Tier 2 & 3 cities in India, largely comprising of foldable smartphones and newly launched smartphones (OnePlus Nord 3 5G, Samsung Galaxy M34 5G, Motorola Razr 40 Series, Realme Narzo 60 Series and iQOO Neo 7 Pro 5G). Multiple new products were launched this Prime Day, by brands such as OnePlus, iQOO, Realme Narzo, Samsung, Motorola, boAt, Sony, and more in India.
Despite the high demand and new product launches, Amazon’s price reductions in the Consumer Electronics category averaged only 0.9% across 27% of its assortment. Similar to what we observed in the Home & Furniture category, this can be attributed to the prevailing high average discount of 44.8% the week prior to Prime Day. Essentially, in Consumer Electronics, shoppers needn’t always wait till sale events like Prime Day to view the most attractive deals. Several are offered even during the days leading up to the sale.
Across subcategories, Earbuds (2.4%), Wireless Headphones (1.6%), and TVs (1.3%) received the highest price reductions due to their popularity and high sales volumes during sales events. On the other hand, Smartwatches (0.6%), Drones (0.4%), and Smartphones (0.3%) had lower markdowns.
In terms of price reductions across brands, Da Capo (52.6%), Muzen (33.3%), JLab (23.6%), and Earboss (21.5%) offered the most attractive deals in the Consumer Electronics category. Notably, Amazon Basics also offered modestly attractive deals (12.2%), highlighting Amazon’s strategy of promoting in-house brands.
The Consumer Electronics category has a loyal shopper base, but generic search keywords like earbuds, headphones, and tablets remain essential for attracting high-intent shoppers and increasing brand awareness. So when it comes to Share of Search, Noise in Smartwatches, Samsung in Smartphones and Tablets, and HP in Laptops, all made strong strides in building their discoverability on Amazon during Prime Day.
Xiaomi in Laptops, Ekko in Earbuds, OnePlus in Smartphones and Apple in Tablets, lost out to other brands during the sale.
Health & Beauty
Health & Beauty emerged as the top-performing category in terms of additional discounts during Prime Day in India. Our data shows that Amazon offered an average additional discount of 5.3% on almost half of its products (46.8%) in this category. Competing head to head with Amazon in this category, Flipkart offered 5.5% additional discounts across 35.8% of its assortment.
Within all the subcategories we analyzed, Sunscreen (7.5%), Make-Up (7.2%), Shampoo (6.6%), and Moisturiser (6.4%) saw the highest price reductions on Amazon. Conversely, staple items like Toothpaste (3.%) and Beardcare (3.6%) had lower markdowns.
During the sale event, brands like Sadhev (43.4%), Clear (41.1%), Teenilicious (40.4%), and Coal Clean Beauty (38.4%), offered the most attractive deals.
In terms of significant gains in Share of Search for brands, L’Oreal Paris in Shampoo and Conditioner led the pack along with Oracura in Electric toothbrushes and The Formularx in Moisturiser. Perfora in Toothpastes and Ustraa in Beardcare also gained more than 10% in their Share of Search during the sale event.
Other popular brands like Tresemme in Conditioners, and Swiss Beauty in Make-Up surprisingly had reduced visibility among the top search results for relevant subcategories.
Navigating the Competitive Landscape: How To Thrive During Sale Events
Amazon’s strategic pricing during Prime Day reflects a balance of profitability, inventory, and competition. Competitive pricing insights empower retailers to make informed decisions, optimize strategies, and thrive during high-stakes events. Prime Day serves as a crucial opportunity to drive sales, attract new customers, and boost loyalty. Therefore, monitoring competitor prices accurately, at scale, is essential for impactful pricing strategies.
For more insights on staying ahead during sale events, reach out to us today!
If you’d like to learn about Amazon’s pricing and discounts during Prime Day 2023 in the US, check out our analysis here.
Amazon’s Prime Day this year proved to be a record-breaking success, becoming the largest Prime Day event in the company’s history. Over the two-day extravaganza, shoppers in the US spent a staggering $12.7 billion, a 6.1% increase from the previous year. Amid inflationary pressures and supply chain disruptions, Amazon adopted a bold discounting strategy, offering steeper discounts compared to Prime Day 2022.
An interesting aspect of Amazon’s approach is their loyalty based offerings. In the weeks leading to Prime Day on July 11-12, members of the loyalty program were given access to “invite-only deals” where shoppers could request invites to specific products that they were looking to purchase on deals. Overall, Amazon’s pricing and discount strategies during Prime Day were carefully designed to create a buzz among shoppers, generate increased sales, and maintain a competitive advantage in the market.
While Prime Day is Amazon’s showstopper, it’s interesting to also see how other leading retailers respond to such a massive sale by their biggest competitor. Do they also lower their prices during the event, or are they happy to take a backseat? To answer these questions, we leveraged our proprietary data aggregation and analysis platform to analyze the prices and discounts of Amazon and its leading competitors across key product categories – Apparel, Home & Furniture, Consumer Electronics, and Health & Beauty – during Prime Day.
Since products on Amazon and other eCommerce websites are often sold at discounts even on normal days not linked to a sale event, we delved into the real value that Prime Day offers to shoppers by focusing on price reductions or additional discounts during the sale compared to the week before. As a result, our approach highlights the genuine benefits of the event for shoppers who count on lower prices during the sale.
Research & Methodology
For our analysis, we tracked the prices of a large number of products across several leading retailers during Prime Day as well as the week prior to the event. The details of our sample are mentioned below:
Number of SKUs: 110,000+
Websites: Amazon, Walmart, Target, Overstock, The Home Depot, Wayfair, Ulta Beauty, Sephora
Categories: Apparel, Home & Furniture, Electronics, Health & Beauty
Pre-event Analysis: 4-10 July 2023
Prime Day Analysis: 11-12 July 2023
Our Key Findings
Our data reveals that Amazon’s price reductions were most aggressive in the Consumer Electronics category, with an average price reduction of 10.4% on Prime Day, due to the category’s popularity and high demand.
The Health & Beauty (6.7%), Apparel (5.9%), and Home & Furniture (4.8%) categories offered relatively modest deals during the sale event.
Below, we delve deeper into our analysis of each category to better understand how price reductions were distributed across key subcategories on Amazon as well as the discounting strategies of Amazon’s leading competitors.
Apparel
As Amazon grappled with surplus inventory, heightened storage costs, and reduced profit margins in apparel (like most other retailers), its average discount before Prime Day was already as high as 13.3%. Then, on Prime Day, Amazon’s apparel deals were tempered at around 5.9% across an impressive 33.1% of its assortment, while Target and Walmart chose not to compete in a meaningful way.
Unlike Prime Day 2022, when Target competed with Amazon with high discounts, the retailer offered only 0.8% additional discount across 4.4% of its assortment in this category. Walmart, too, reduced its prices by only 1.4% on 8.5% of its assortment during Prime Day.
Across all the apparel subcategories we analyzed, Women’s Athleisure (8.7%), Men’s Swimwear (8%), and Women’s Tops (7.6%) were among the ones with the highest price reductions. On the other hand, Men’s Athleisure (2.5%), Women’s Shoes (3.5%), and Men’s Innerwear (4.1%) had conservative markdowns.
Pricing decisions across the various subcategories are likely to have been influenced by several factors like inventory levels, demand patterns, and the need to balance competitive offers with maintaining reasonable profit margins, as Amazon tried to cater to a more price-sensitive consumer.
Across all apparel subcategories, leading brands that offered the highest markdowns were Tommy Hilfiger (11.5%), Amazon Essentials (9.4%), Adidas (8.6%), and Calvin Klein (8.6%).
For brands, however, lowering prices is only one lever to attract and convert shoppers. They also need to ensure they’re highly visible and discoverable on Amazon’s search listings. This exponentially improves their chances of driving more clicks and conversions. In our analysis, we tracked the Share of Search of brands across several popular search keywords. Share of Search for a brand is defined as the proportion of the brand’s products in the top 20 search results for a search query.
Our data indicates that several brands gained impressive ground in their discoverability during Prime Day, while others fell behind. Gildan in Men’s Innerwear, Adidas in Men’s and Women’s Shoes, Anrabess in Women’s Athleisure, and Lululemon in Men’s Athleisure, among others, improved their Share of Search by significant levels during Prime Day.
On the other hand, brands like Hanes in Men’s and Women’s Innerwear, Kanu Surf in Men’s Swimwear, Cupshe in Women’s Swimwear, and others lost around 10% in their Share of Search during the event. This is likely to have impacted their sales volumes adversely.
Home & Furniture
The Home & Furniture industry has been challenged with reduced demand due to inflationary pressures over the past year or so. Leading retailers in the category overestimated the demand, leading to overstocking of inventory. As a result, Home & Furniture is one of the few categories that saw Amazon’s competitors participate at a significant level on Prime Day in order to ensure they don’t fall behind on liquidating their stock.
Amazon’s additional discounts averaged 4.8% across 30.2% of its assortment. Wayfair and Overstock too reduced their prices by 4.8% and 4.3% on around 44% of their respective assortments. Wayfair’s move is likely a part of their strategy to attract new customers and expand their market share, in response to a decline in their consumer base. Last year, Wayfair experienced a loss of 5 million out of its 1.3 billion consumers due to weakening demand.
Target and Walmart did offer additional discounts, but they were not at a competitive level. The Home Depot effectively opted not to compete at all during the sale event. Overall, the pricing actions of these retailers are in stark contrast to the highly conservative pricing strategies observed on Prime Day last year.
Our recent pricing analysis of the Home & Furniture category revealed more interesting insights and pricing dynamics over the past year.
Across all the subcategories we analyzed, Bookcases (8.2%), Rugs (7.8%), Mattresses (6.5%), and Luggage (6.2%) were among the ones with high price reductions.
Meanwhile, Sofas (2.4%), Washer / Dryers (2.4%), and Entertainment Units (2.7%) had lower markdowns. These are large and substantial purchases, making retailers more cautious about deeply discounting them while still ensuring profitability.
The brands that stepped up and offered the highest markdowns in this category include Zinus (20.2%), Comfee (10.8%), Sauder (9.9%), and Best Choice Products (8.7%).
In terms of Share of Search, Rockland in Luggage gained the highest (21%), followed by Farberware in Dishwasher, Olee Sleep in Mattresses, and Homeguave in Mattresses gained significant ground in their respective categories as shown in the image below.
Brands like Best Choice Products in Coffee Tables, Molblly in Mattresses, and Black+Decker in Washer/Dryers and Dishwashers lost a good portion of their Share of Search during the event. Due to high competition for visibility during sale events, brands that fail to keep an eye on their Share of Search stand to take a hit in their sales, especially in categories like Home & Furniture that tend to have low brand loyalty.
Consumer Electronics
2023 was the year of consumer electronics on Amazon Prime Day. Amazon’s price reduction during the sale averaged 10.4% across 54.5% of its assortment in the category. Target and Walmart, on the other hand, offered significantly lower additional discounts of 1.9% and 2.7% on 10.4% and 19.1% of their assortment, respectively.
The consumer electronics category often witnesses aggressive price reductions during Prime Day and other sale events due to its popularity and high demand. In addition, since retailer margins are usually low in this category, shoppers often have to wait for sale events like Prime Day (when brands markdown their wholesale rates) to have several attractive deals to choose from.
Across all the subcategories we analyzed, Smartwatches (15.4%), Wireless Headphones (15.4%), Earbuds (14.9%), Headphones (12.5%), and Tablets (12.0%), were among the ones with the highest price reductions. All of these subcategories are quite popular that tend to sell in large volumes during sale events.
Meanwhile, Laptops (2.1%), TVs (3.1%), and Smartphones (7.6%) had lower markdowns. A lower markdown on smartphones may reflect steady demand throughout the year, reducing the urgency to offer significant discounts during the short Prime Day window.
Amazon (22%), Tozo (12.5%), Lenovo (10.8%), JBL (8.3%), and Apple (5%) offered the highest price reductions in Consumer Electronics as a whole. Clearly, Amazon didn’t hold back on offering attractive deals on its own private label products in this category.
Consumer Electronics as a category tends to have a brand loyal shopper base. However, Share of Search generic search keywords are still very important for keywords like earbuds, headphones, and tablets that result in relatively lower priced products. HP in Laptops, Samsung in Tablets and TVs, and Oneplus in Smartphones all made strong strides in building their discoverability on Amazon during Prime Day. Beyond just driving more sales, this also has the intended effect of boosting brand awareness among high-intent shoppers.
Sony in Headphones, Asus in Laptops, and Insignia in TVs lost out to other brands in terms of their discoverability during the sale. Sony and Asus, especially would be hurting as they are prominent brands in their respective categories.
Health & Beauty
The Health & Beauty category is a favorite among consumers during Prime Day, as it encompasses a wide range of products like skincare, cosmetics, and grooming items. As shoppers often tend to stock up during the sale, brands and retailers are willing to offer competitive discounts and gain an edge over their competitors.
Our data reveals that the average additional discount on Amazon was 6.7%, offered on a little over a third of its assortment. Walmart reduced its prices sizably as well, by an average of 3.1% on 13.4% of its assortment.
Interestingly, Sephora and Ulta Beauty, leading retailers in the Health & Beauty category did not compete on price at all this Prime Day. It is likely they are confident their loyal customer base will not be influenced by Amazon’s Prime Day deals and be driven away merely by lower prices. In addition, keeping their prices steady during Prime Day might have been a strategic choice to protect their brand reputation and premium positioning.
Relatively premium subcategories like Electric Toothbrushes (10%), Moisturizer (8.3%), Beardcare (7.3%), and Make Up (6.7%) saw the highest price reductions on Amazon.
In contrast, staple items like Toothpaste (3.7%), Shampoos (5.4%), and Conditioners (5.7%) had lower markdowns.
Among the leading brands in this category, Oral-B (10.3%), Philips Sonicare (8.7%), Neutrogena (8.4%), and Colgate (5.6%) offered the most attractive deals during the sale event.
In terms of significant gains in Share of Search for brands, Oral-B in Electric Toothbrushes led the pack again. Neutrogena in Sunscreens and Somall in Toothpastes also gained more than 10% in their Share of Search during the sale event, followed by Tresemme in Shampoos and Airspun in Make-Up products.
Other popular brands like Crest in Toothpastes, e.l.f in Make-Up, Philips Sonicare in Electric Toothbrushes, and Sheamoisture in Beradcare surprisingly had reduced visibility among the top search results for relevant subcategories.
Staying Ahead of the Curve During Sale Events
This Prime Day, Amazon leveraged its scale to offer aggressive discounts across key product categories, while several competing retailers chose to sit back and let the sale play out. Others chose a selective discounting strategy that focused their modest price reductions on a small set of items.
At DataWeave, we understand the pivotal role competitive pricing insights play in empowering retailers and brands to gain a competitive edge, especially during crucial events like Prime Day. For retailers, the ability to track competitor prices accurately, at scale, in a timely manner is essential to plotting and acting on impactful pricing strategies and staying ahead of the curve.
Contrary to popular speculation of lukewarm online sales owing to the weak economy, high inflation, and stretched wallets, Black Friday this year recorded a whopping $9 billion in e-commerce sales. Despite the lull in online shopping across many retailers in the months preceding Thanksgiving and weakened consumer sentiment, US online merchants saw a sizable boost in sales during and after Thanksgiving, albeit at a slower growth of 2.3%, as reported by Adobe Analytics.
This article looks closely at the Black Friday data to understand which brands, retailers, and product categories were key players. Through DataWeave’s innovative Digital Shelf Analytics product, we deep dive into the availability, discount, and share-of-search data to deduce why some product categories and retailers fared better than others.
Who: Retailers and Brands that had the Highest Presence
Black Friday sales this year were driven by consumers grabbing the biggest and best deals to make the most of their already stretched wallets. Many shoppers opted for flexible payment schemes, and Buy Now Pay Later (BPNL) payments rose by 78% compared to the week before Thanksgiving. Surprisingly, Amazon, which was the most searched retailer during Black Friday last year, came only fourth this year, as reported by the Search Intelligence company, Captify.
According to Captify, Walmart was the most searched retailer for Black Friday deals, followed by Target, Kohls, and Amazon in that order. Amazon, however, has reported its biggest Thanksgiving sale this year, with independent retailers selling through Amazon seeing a total sales of $1 billion. With the economic slowdown and thin wallets looming large, discount rates greatly influenced consumer spending. Mobile shopping accounted for 55% of digital sales, 8.5% more than the previous year.
As told by Adobe, Electronics were the significant sales driver, reporting 221% higher sales than in October this year, with smart home items and audio equipment playing an important role with 271% and 230% higher sales. Toys ( popular purchases were Fortnite, Roblox, Bluey, Funko Pop!, and Disney Encanto) and exercise equipment also registered a substantial growth of 285% and 218%, respectively.
Other top-selling items included gaming consoles (Xbox Series X and PlayStation 5 devices, games including FIFA 23, NBA 2k23, and Pokemon Scarlet & Violet), drones, Apple MacBooks, and Dyson products (airwrap and vacuum). Amazon’s most popular items were reported to be Apple Airpods, Nintendo Switches, Echo Dot smart speakers, and Fire TV sticks.
What: Top Selling Product Categories
Electronics, closely followed by home appliances (robotic vacuum cleaners), toys, and exercise equipment, were popular product categories in demand during Black Friday this year. Several retailers, including Amazon, Walmart, Target, Kohls, BestBuy, and Home Depot, offered lucrative pre-Black Friday discounts to trigger early sales kick-off.
Amazon carried an early discount of 50% on its Echo smart speaker, Target offered 30% off on Dyson vacuum cleaners, Walmart offered 25-35% off on Apple ipads and watches, and Kohls offered 51% off on the iRobot Roomba.
Amazon’s top ten best-selling products ranged from Amazon devices like Echo Dot speakers, Fire TV sticks, and Echo Show to Apple AirPods, Nintendo Switches, New Balance sneakers, Champion Apparel, and Burt’s Bees Lotions. The popular product categories were home, fashion, toys, beauty & health, and Amazon devices. Consumers heavily supported small businesses, contributing to $1 billion in sales. Top sellers from small businesses included card and board games.
Briefly correlating the discounts offered with the best-selling product categories, one can notice that the deals have largely influenced Black Friday sales this year. Popular categories are those that have had deep discounts, reflecting the consumer’s tendency to wait and grab the best deals.
How: Role of Digital Shelf Analytics – Key Performance Indicators
DataWeave’s Analysis Methodology
We have seen a summary of the Black Friday 2022 statistics – sales recorded, top-selling products, product categories, and retailers. Using DataWeave’s e-commerce analytics product, we track and study the variations in digital shelf KPIs across retailers before Thanksgiving and during Thanksgiving to understand how these influence sales.
Availability scores, discount rates, and share of search data are analyzed for top retailers in the US for key product categories. Data is tracked and analyzed across two time periods – before Thanksgiving (Nov 10 – Nov 21) and during Thanksgiving (Nov 21 – Nov 25).
Methodology
Retailers tracked: Amazon, Best Buy, Sephora, Target, Ulta, Walmart
Product Categories tracked: Electronics, Home Improvement, Beauty, Furniture
Digital Shelf KPIs tracked: Availability, Discount rates, Share of Search
Location: USA
Amazon maintained good availability across all product categories – Beauty ranks the highest.
Salient Insights
Amazon maintained good overall availability – an improvement of 3% over Prime day.
Beauty had the highest availability of 95%, with none and Lotion & Brushes reporting 97% and 95% availability, respectively. Shampoo reported the lowest availability at 92%
Home Improvement had the least availability at 87%, with dishwashers (68%) and washers and dryers (78%) having the lowest availability.
Unlike Furniture and Home Improvement, most categories maintained similar availability scores before and during Black Friday.
Furniture improved its availability during Black Friday by 4%, while Home Improvement reported a decrease in availability during Black Friday by 4%.
Electronics, which was a major sales driver, had an availability of > 90% across all sub-categories except for Television, which had a low availability of 70%
Tables and chairs registered 99% availability under Furniture
The above data indicates that Amazon ensured the high availability of utility products that consumers would buy even during a slow economy. Other retailers showed similar availability trends, with scores being similar prior to and during the event.
Discounts Drove Sales – Best Buy offered the Highest Discounts
Highlights
Best Buy offered the highest early Black Friday discount of 30%, followed by an additional 9% discount around Black Friday. Walmart followed next with 21% early discounts and an extra 4.5% discount during the event. Amazon came next with 17% early discounts and a 5% discount during Black Friday. Discount rates seem to strongly correlate with online searches, with Walmart beating Amazon this year as the most searched retailer for Black Friday deals.
Electronics was the most discounted category across Amazon, Best Buy, and Target, with an average discount of 21%. Walmart gave lower deals on electronics (12%). Electronics also had heavy early discounts of 12%, with most retailers giving an additional discount of 7-8% closer to Thanksgiving.
Best Buy offered early discounts of 10% and further upped their discounts by another 12% closer to Thanksgiving. Being the most discounted category, electronics was also a significant sales driver this Black Friday.
Amazon offered the highest discounts for Beauty products (18%), followed by Ulta at 10% and Walmart at 8%. Sephora and Target gave minimal discounts on beauty products (3%)
Best Buy gave the maximum discounts on Home Improvement products (16%), followed by Amazon at 14%. Walmart gave much lower discounts of 7% on Home Improvement products.
Furniture is another category with 12-13% discounts at both Amazon and Walmart.
Best Buy’s strategy this year has been to offer heavily discounted early deals to boost their sales.
Airpods and headphones were the most discounted item under Electronics, with Amazon and Target offering a whopping 27-29% discount. This clearly resulted in heavy sales of AirPods this Thanksgiving.
Best Buy and Target had good discounts on all electronic items, while Amazon gave heavier discounts on AirPods, headphones, and smartwatches.
Walmart did not offer hefty discounts on laptops and headphones, instead focused on Smartwatches, smartphones, and television.
In Home improvement, Best Buy offered the biggest discounts for refrigerators, washers and dryers, and dishwashers, while Amazon focussed more on Tools.
Walmart did not offer many discounts in this category.
Amazon topped the discount charts for maximum combined discounts for makeup and hair brushes on the day of the event.
All retailers offered better discounts for utility products like tables, chairs, and cots (~15-17%), while dressers and couches carried lower discounts (~6-10%).
Highlights
Different companies had different discount strategies based on price buckets.
Amazon gave heavier discounts in the lower price buckets (< 200$) and lower discounts for products priced higher than 200$.
Best Buy offered the heaviest early discounts of >25% on products priced under 20$ but provided a few additional discounts during the event. For products priced higher than 20$, Best Buy uniformly offered substantial early discounts as well as further discounts during the event.
On the other hand, Target focussed on mid and high-priced items, offering heavy early discounts of 16-18% on products priced higher than 100$ and early discounts of ~7% for middle and lower-priced items. For middle-priced products (40-100$), it offered heavier discounts of 10-12% during the event.
Walmart focussed on mid-priced products, offering the highest discounts (both early (~12%) and additional discounts (5%)). It offered the least discounts (~8-9%) on products priced higher than 200$.
Share of Search – Amazon is the only retailer with sponsored searches; Apple AirPods rule the roost.
Salient Insights
Amazon is the only retailer with sponsored searches, with HP, Lenovo laptops, and Apple AirPods occupying the highest share. This correlates with AirPods being one of the most sold products.
HP laptops had the highest share on Amazon pre-Event but gave the spot to Lenovo during Thanksgiving.
Tracphone and Motorola smartphones, Insignia Televisions, and JBL headphones had a good SoS on Amazon.
On Best Buy, HP and Dell laptops featured most in searches, with HP ruling the roost during the event. Lenovo had a small presence.
Samsung smartwatches, televisions, and Apple AirPods have a big chunk of the search at Best Buy.
On Target, pop sockets, smartphones, Apple smartwatches, headphones, and AirPods have the most prominent presence. Apple was the most featured brand in this segment.
Note: The share of search percentage reported here is the average score across all subcategories (makeup, lotion, shampoo, hair dryers and hair brushes) of Beauty.
Salient Insights
Amazon, Target, Sephora, and Ulta sold beauty products, with Amazon being the only retailer with sponsored products.
Ogx, bs-mall, conair, hywestger were popular brands on Amazon, with interest-based ads occupying a substantial part of the search results, especially in Lotions (~40-50%)
Tresemme, Scotch Brite, Revlon and Cerave were popular brands in Target
Dyson products (brushes and hair dryers) are featured at Ulta’s top of the search, followed by Pureology shampoos.
Sephora’s own collection of brushes featured prominently on their website both before and during the event, followed by Dyson and T3 brushes and hair dryers.
Note: The share of search percentage reported here is the average score across all subcategories (refrigerator, washers/dryers, dishwashers, tools and coolers) of Home Improvements.
Salient Insights
In Amazon, Frigidaire and RCA had the highest SoS amongst Refrigerators, and LG occupied the highest share among washers and dryers, Coleman in Coolers, Dewalt in Tools, and Comfee in Dishwashers, both before and during Black Friday.
In contrast, on Best Buy, Samsung had the highest share of SoS amongst Refrigerators, package deals were most prominent in washers and dryers, LG among dishwashers, ifixit in Tools, and Corsair in Coolers.
Note: The share of search percentage reported here is the average score across all subcategories (bed, chair, couch, dresser, and table) of Furniture.
Salient Insights
Interest-based ads occupied the highest SoS on Amazon for Beds.
Urban shop and Amazon basics were popular in Chairs, Lifestyle in Couches, WLive in Dressers both before and during the event.
Vasagle was more popular during the event than Furrion in Tables, though the reverse was true prior to the event.
Summary & Key Takeaways
Black Friday this year was a pleasant surprise to Brands and Retailers, reporting much larger sales than predicted. After experiencing a slump in sales in the months leading up to Thanksgiving, e-commerce vendors have a reason to be optimistic about their holiday season sales forecasts.
A record-breaking $9.2 Billion in online sales was reported by Adobe Analytics, a growth of 2.3 % compared to the previous year.
Mobile shopping accounted for 55% of digital sales, a rise of 8.5% compared to last year.
Retailers wooed customers through deep discounts (~30%) prior to Thanksgiving and around Black Friday. Heavily discounted items like Apple AirPods were the most popular.
Thanks to inflation and stretched wallets, consumers were willing to spend but waited to grab the best and biggest deals. Utility products had better sales.
With tough competition between retailers on who offers the best discounts, Amazon slid down to the fourth position, and Walmart was the most searched retailer.
DataWeave, through its Digital Shelf Analytics and Commerce Intelligence solutions, gleans useful insights from e-commerce data and breaks down trends during global shopping events like Prime Day, Black Friday, and Cyber Monday. If you are a brand or a retailer who would like to know more about our products and solutions, contact us at contact@dataweave.com.
In the age of a mature eCommerce and omni-channel retail ecosystem, pricing is the premier competitive battleground. It’s both the biggest offensive weapon to capture market share – and the biggest vulnerability if you stumble. In fact, a recent Statista survey revealed that 70% of US online users prioritize competitive pricing in their digital shopping choices. Yet most retailers still struggle with consistent, profitable pricing often replying on instincts rather than data-led intelligence.
That’s where Pricing Intelligence (PI) comes in. PI is a fast-evolving discipline powering data-driven, continually optimized pricing strategies to help merchants make rapid, surgical adjustments that attract customers and protect margins. Most retailers are aware of Pricing Intelligence tools, but they miss out on getting one that serves their needs and proves its ROI consistently.
Because of course, not all pricing intelligence solutions are created equal. Here’s top features retailers looking to invest in a Pricing Intelligence tool should look out for.
1. Accurate Product Matching
Of course, accurate pricing data is table stakes for any PI solution –The core premise of any pricing intelligence tool is enabling robust product tracking and price monitoring of your own catalog against the competition.
So, a PI tool must take care of matching each of your product across all other sources, so that you can make a straightforward comparison and take actions.
But since the internet is not a one standard entity and even the same or similar products can have different titles, descriptions, specs and images, most retailers end up capturing incomplete or inaccurate data completely undermining their intelligence. A good Pricing Intelligence tool like DataWeave’s should be able to leverage Similarity Matching and AI-based image tracking to bring more products under product matches and present a more complete picture.
2. Width of pricing types and factoring in real net effective prices
Product accuracy must extend far beyond just basic “landed” or “street” pricing and cover more types of specialized pricing situations. A robust pricing intelligence tool should automatically detect and handle nuanced mechanics like:
– Bundled/kit/packaged pricing
– Pricing regulated by manufacturer policies (MSRP, MAP, etc.)
– Inventory-level or stocking threshold-based pricing
– Zonal/regional taxes, fees and price variations
– Segment-based pricing for members, loyalty tiers, etc.
– Pricing tiers or breaks based on volume/purchase quantities
Properly capturing and classifying these additional pricing nuances by retail vertical is key. Otherwise you’ll have major blind spots and inaccuracies that leave you open to being undercut or overpriced compared to real-world market dynamics.
3. Real-Time, Continuous Monitoring and High Data Update Frequency
Data points like product prices and offers get stale fairly quickly. Ideally, we want to see real time data. Real time is not achievable at scale, or might even be an overkill in many cases.
However, an effective PI tool must present up-to-date data to the extent possible. Based on requirement this can vary from a day to a few hours thus helping the business stay ahead of the price curve.
4. Scalable Coverage and Contextual Enrichment For Full Product Information
For many retailers, one of the biggest pricing intelligence challenges is scaling comprehensive, accurate monitoring across their full product catalog and relevant competitor ecosystem. This is especially true for those operating regionally or with multiple banners/brands.
You need robust data collection capabilities to ingest and process pricing data on everything from big box retailers and national sellers all the way down to small mom-and-pop shops that may only sell locally – but could still impact your pricing perception.
A best-in-class PI solution should have the ability to dynamically monitor millions of products and tens of thousands of competitor sources globally, processing all those inputs in a normalized, unified way. Additionally, your PI solution needs to be flexible to adapt seasonal or special requirements – whether that involves tracking key value items more frequently, or getting updates on pricing changes during festive seasons.
But beyond just raw data collection scale, leading PI solutions also enrich and add context around that pricing data to make it far more actionable through technologies like:
– Machine learning models to extract intelligent insights
– Semantic processing to identify nuanced pricing mechanics
– Competitive product knowledge graphs to map relationships
– Location data appending for geographic/zonal context
This enrichment bridges the gap between simple “list prices” and real-world factors like localized promotions, inventory levels, demand elasticity and other variables that should be driving more nuanced, profitable pricing decisions.
5. Pricing Opportunities
A good PI tool should present data at different levels of granularity: category, sub-category, brand, and individual product. This helps the category/merchandizing team or the pricing analysts to surgically strike problem areas. For instance, when you are tracking 1000s or even 100s of products, it’s next to impossible to go over every product and take pricing decisions.
Furthermore, with large, diverse product catalogs, it’s impossible for category managers to manually monitor pricing on every SKU. Your pricing intelligence tool must automatically analyze and highlight prioritized pricing opportunities where action is needed – enabling efficient pricing decisions at a glance.
6. Historical Pricing
“Prediction is very difficult, especially if it’s about the future.” But they also say, history can be a useful predictor of the future. Nowhere is it truer than in competitive price intelligence.
An analysis of historical data almost always shows a trend that can be capitalized on for competitive pricing. A good PI tool stores and presents historical pricing data in a useful manner.
7. “It’s not [just] about the money”
Retail is a highly competitive and commoditized sector. So, price is an important factor for a consumer when making a decision to buy a product. Having said that, as a retailer, you don’t always want to compete on pricing.
You may want to compete through better packaging, or giving the user more choice (variants/colours/sizes), or better SLAs. This is where a Price Intelligence tool needs to go beyond just pricing. It needs to capture and present all other relevant data points associated with a product.
8. Uncluttered User Experience
Any tool built for a user needs to be usable, intuitive, and uncluttered. More so for busy managers who need to take several decisions quickly day on day. A Price Intelligence tool is in essence a Data Product. A data product is built on top of a lot of data; however, a good data product is one “where data recedes to the background”.
A data product is not one that delivers a lot of data, but one that delivers actionable data and insights based on data. Data presentation is also another important aspect. A good PI tool delivers the most important data points in formats and templates that a customer can easily consume.
DataWeave provides Competitive Intelligence for retailers, brands, and manufacturers. It is built on top of huge amounts of products data to provide features such as: pricing opportunities (and changes), assortment intelligence, gaps in catalogs, reporting and analytics, and tracking promotions, and product launches.
DataWeave is powered by distributed data crawling and processing engines that enables serving millions of data points around products data refreshed on a daily basis. This data is presented through dashboards, notifications, and reports. PriceWeave brings the ability to use BigData in compelling ways to retailers.
PriceWeave lets you track any number of products across any categories against your competitors. Still not convinced? Try us out. Just send us a request for a demo.
Online reviews have revolutionized how customers purchase products and services. In fact, eCommerce success for certain products hinges on the ratings and reviews. With this, have come the pitfalls of corruption in eCommerce.
New brands trying to establish a presence and capture critical mass have been known to resort to soliciting fake and paid reviews to uplift their brand in search rankings. Similarly, these brands can also encourage fake negative reviews on competitor’s listings to bring down their value. Bots and paid manual reviews are usually employed to rake up the review count. Review sites like TrustPilot, Google Reviews, and marketplaces like Amazon are littered with fraudulent reviews. In fact, Guardian calculated that 3.6% of all reviews on TripAdvisor were fraudulent. According to a 2021 report by Statista, 46% of the 2.7 million online fake reviews that were removed were five-star reviews!
Fake online reviews are misleading since customers shopping both online and offline rely on reviews to make purchase decisions. Fake reviews also pose further problems because they deceive consumers into spending money on a product or with a company they may not have otherwise chosen.
Federal Trade Commission (FTC) made a recent announcement to send penalties to over 700 brands and retailers for fake endorsements and reviews. While this notice references influencer content and testimonials, it also applies to customer reviews.
In this blog, we will discuss the importance of reviews for brands and retailers, spotting fake reviews on Amazon, and steps that eCommerce companies can take to tackle fake reviews.
Customers are more likely to purchase if other customers, even strangers, agree that it was a great purchase. Reviews also make brands more visible.
Why are fake online reviews so resilient?
A significant reason is that the ROI of getting fake reviews increases profitability & sales multifold. For example, an extra star on Yelp can increase a restaurant’s revenue by 5% to 9%. FTC has said that the expenditure on fake reviews can provide a 20x return. However, fake and incentivized reviews are a huge problem. Amazon, one of the largest eCommerce marketplaces, banned incentivized reviews in 2016. It took down suspicious reviews and has taken legal action against sellers who violate its policies.
Online Reviews
How to Spot a Fake Review on Amazon
Marketplaces, Google, and review sites like Yelp can get hundreds of thousands of reviews daily. In a survey by PCMag that interviewed 1,000 US shoppers who looked forward to shopping on Prime Day 2020, only 16% were very confident about detecting fake Amazon product reviews, and 24% were confident they could do it. The rest of the survey respondents were somewhat or not confident they could pick out the fakes on Amazon. Here are our best tips for spotting fake reviews on marketplaces like Amazon:
Duplicate Content: If you notice dozens of reviews with the same description and title as if they were copied and pasted multiple times, they’re most likely fake reviews.
Multiple Reviews on the Same Day: Another identification of fake reviews is when there are dozens or multiple reviews on a single day. There can be a bunch of both positive and negative reviews for products.
Unverified or Anonymous Reviewers: You can see if the review is from a verified buyer on Amazon. Brands can also check if they have any record of the reviewer’s purchase to weed out fake reviews.
Incorrect Language: Fake reviews can come from people outside your country. If you notice multiple reviews with similar incorrect words and common errors, there is a good chance those reviews are fake, and someone paid the reviewer to write them.
What can eCommerce brands do to protect themselves against fake reviews?
Follow a zero-tolerance policy for fake reviews.
The major step is to ensure that fake reviews are never posted on your site. Allowing fake reviews negatively affects your business and your bottom line. You can hire a third-party UGC moderator that uses data-driven, anti-fraud methods to evaluate reviews. It will be a much more successful and quicker step in protecting your brand’s reputation.
Don’t screen out negative reviews.
While receiving a negative review might be the worst nightmare, they’re necessary for a successful UGC program. Customers are more likely to purchase from a business that responds to all reviews, including negative reviews. Customers said that negative reviews have more detailed product information, while 32% of those customers think they’re less likely to be fake. Besides, brands that respond to negative reviews gain customers’ trust and loyalty. Here are some Tips on how to Respond to Negative reviews online.
Be transparent about how you collect UGC.
Brands can ensure that their customers trust user-generated content by being honest about how they collected it. Companies should never ask for paid or incentivized positive reviews. Instead, brands should empower their customers to leave honest feedback. If you’re offering free products, a chance to win something, or discount coupons in exchange for an unbiased review, then the review should specify how it was collected. For example, you can add indicators like “this reviewer received a coupon or a free product in exchange for honest feedback.
Maintain trust
Having fake reviews causes a loss of trust, with many consumers believing that they have seen fake reviews for online and offline businesses. Removing fake reviews doesn’t only help with revenue and brand trust, but it also helps brands to maintain trust among their existing and future customers.
Conclusion
Fake reviews are one of the biggest reputation killers and a huge problem for eCommerce platforms, brands, and customers. Brands must take the necessary steps to minimize the risk of fake reviews and expand businesses among authentic users. Although modern text generation tools are becoming more competent in writing realistic reviews, there are AI- and ML-backed tools that can accurately detect reviews written by other machines.
Need help tracking your online ratings & reviews? Or decoding customer sentiment from reviews they’ve left for your products? DataWeave offers a customizable and scaleable data solution to analyse ratings and reviews for online retailers and brands vis v vis their competitors. Sign up for a demo with our team to know how DataWeave can help.
After last year’s blistering pace of e-commerce sales growth in the home category, we at DataWeave wanted to know how Prime Day 2021 discounts on home products would impact retailers and brands around the world.
We focused our analysis on how international retailers adapted their Prime Day pricing strategies to distinguish their offerings across eight home subcategories, including bed & bath, kitchen and pet supplies.
Our Methodology We tracked the pricing of products among 21 leading retailers in nine countries across five regions, including:
• The US (Ace Hardware, Amazon US, Best Buy, Home Depot, Lowe’s, Petco, PetSmart, Target and Wayfair US) • The UK (Amazon UK, Ebay, Etsy, OnBuy and Wayfair UK) • Europe (Amazon France, Amazon Germany and Amazon Italy) • The Middle East (Amazon Saudi Arabia and Amazon UAE) • Asia (Amazon Japan and Amazon Singapore)
The results showed some surprising differences among retailers and regions. See how retailers used pricing as a competitive strategy to win Prime Day sales in the home category, as well as international home brands that stood out for the discounts on their products.
Percentage of items with a price decrease
The US retailer with the overall highest percentage of home products with a price decrease for Prime Day was Amazon US (26.4%).
Home subcategories with the highest percentage of items with a price decrease per US retailer were:
• Ace Hardware: Power & hand tools (21.2%) • Amazon US: Furniture (36.3%), appliances (34.1%) and kitchen (28.3%) • Best Buy: Appliances (0.9%) • Home Depot: Power & hand tools (0.2%) • Lowe’s: Furniture (29.2%), power & hand tools (5.5%) and appliances (4.1%) • Petco: Pet supplies (11.6%) • Target: Bed & bath (37.9%), furniture (32.5%) and kitchen (11.5%) • Wayfair US: Pet supplies (31.9%), home & garden (25.6%) and bed & bath (24.8%)
The UK retailer with the overall highest percentage of items with a price decrease for Prime Day was Amazon UK (36.4%).
Home subcategories with the highest percentage of items with a price decrease per UK retailer were:
• Amazon UK: Appliances (41.7%), power & hand tools (39.5%) and furniture (36.4%) • Ebay: Smart home (10.5%), bed & bath (10.1%) and furniture (8.3%) • Etsy: Bed & bath (1.7%), kitchen and pet supplies (both 1.5%) • Wayfair UK: Kitchen (17.7%), bed & bath (10.8%) and home & garden (5.9)
In Europe, Amazon Germany had the overall highest percentage of items with a price decrease for Prime Day (27.3%).
Home subcategories with the highest percentage of items with a price decrease per European retailer were:
• Amazon France: Appliances (15.9%), power & hand tools (15.8%) and furniture (14.2%) • Amazon Germany: Power & hand tools (40.0%), appliances (33.7%) and pet supplies (28.4%) • Amazon Italy: Furniture (11.8%)
Across the Middle East & Asia, Amazon UAE had the overall highest percentage of items with a price decrease for Prime Day (41.6%).
Home subcategories with the highest percentage of items with a price decrease per retailer were:
• Amazon Saudi Arabia: Power & hand tools (53.8%), pet supplies (33.3%) and appliances (30.4%) • Amazon UAE: Appliances (55.8%), kitchen (49.9%) and pet supplies (49.0%) • Amazon Japan: Appliances (15.3%), power & hand tools (10.8%) and furniture (9.0%) • Amazon Singapore: Bed & bath (35.4%), appliances (30.2%) and power & hand tools (27.2%)
Magnitude of price decrease
The US retailer with the greatest overall magnitude of price decrease for Prime Day was Target (20.3%).
The home subcategories with the greatest magnitude of price decrease per US retailer were:
• Ace Hardware: Power & hand tools (14.3%) • Amazon US: Kitchen (21.0%), appliances and pet supplies (both 18.3%) and furniture (15.1%) • Best Buy: Appliances (8.7%) • Home Depot: Power & hand tools (17.7%) • Lowe’s: Power & hand tools (13.4%), furniture (13.0%) and appliances (10.8%) • Petco: Pet supplies (17.2%) • Target: Bed & bath (28.9%), smart home and kitchen (both 19.1%) and furniture (18.5%) • Wayfair US: Pet supplies (4.6%), kitchen (4.4%) and bed & bath (4.1%)
Brands with the greatest magnitude of price decreases per US retailer included:
• Ace Hardware: Zircon (48.6%), Smith\u0027s (36.1%) and DMT (30.0%) • Amazon US: Supply Guru (56.3%), Seresto (55.4%) and Advantage (53.4%) • Best Buy: Panasonic (29.1%), Farberware (16.6%) and Insignia™ (14.0%) • Home Depot: Husky (17.7%) • Lowe’s: Metabo HPT (43.2%), Dewalt (27.8%) and GZMR (26.5%) • Petco: Seresto (50.0%), Open Road Brands (45.4%) and Starmark (42.1%) • Target: Little Tikes (50.0%), Bobsweep (43.9%) and Shark (42.2%) • Wayfair US: Sorbus (57.8%), GE Appliances (45.9%) and Nu Steel (42.1%)
The UK retailer with the greatest overall magnitude of price decrease for Prime Day was Etsy UK (19.8%).
The home subcategories with the greatest magnitude of price decrease per UK retailer were:
• Amazon UK: Furniture (21.8%), power & hand tools (21.5%) and appliances (20.8%) • Ebay: Pet supplies (12.2%), appliances and furniture (both 12.0%) and bed & bath (10.0%) • Etsy: Pet supplies (44.3%), kitchen (18.1%) and bed & bath (14.2%) • Wayfair UK: Home & garden (12.2%), bed & bath (9.2%) and furniture (8.9%)
Brands with the greatest magnitude of price decreases across home sub-categories per UK retailer included:
• Amazon UK: Tefal (54.0%), Caterpack (51.6%) and Nylabone (49.9%) • Ebay: Bob Martin (59.8%), Fridgemaster (57.5%) and Tetramin (49.3%) • Etsy: Celebnails and vitrifiedstudio (both 49.5%), Deco-Den UK Supplies (46.5%) and Caxo Beauty (36.9%) • Wayfair UK: Breakwater Bay (41.1%), Zipcode Design (33.3%) and Heritage Brass (29.7
Among European retailers, Amazon Italy offered the greatest overall magnitude of price decrease for Prime Day (29.9%) among a total of 49 products.
The home subcategories with the greatest magnitude of price decrease per European retailer were:
• Amazon France: Bed & bath (11.7%), pet supplies (11.2%) and appliance (9.2%) • Amazon Germany: Kitchen (23.4%), power & hand tools (22.3%) and furniture (20.2%) • Amazon Italy: Furniture (29.9%)
Brands with the greatest magnitude of price decreases per European retailer included:
• Amazon France: Thermobaby (47.6%), Sinogoods (44.6%) and Tractive (40.0%) • Amazon Germany: Sage Appliances (56.5%), Nasum (51.7%) and Hikenture (49.2%) • Amazon Italy: Gifort (55.1%) and Wokkol (4.8%)
Across the Middle East and Asia, Amazon UAE offered the greatest overall magnitude of price decrease for Prime Day (15.3%).
The home subcategories with the greatest magnitude of price decrease per retailer were:
• Amazon Saudi Arabia: Furniture (18.0%), pet supplies (15.9%) and power & hand tools (15.8%) • Amazon UAE: Pet supplies (17.8%), appliances (16.4%) and kitchen (16.2%) • Amazon Japan: Kitchen (25.4%), furniture (14.5%) and bed & bath (13.6%) • Amazon Singapore: Kitchen (14.0%), furniture (11.1%) and appliances (8.0%)
Brands with the greatest magnitude of price decreases per retailer in the Middle East and Asia included:
• Amazon Saudi Arabia: American Baby Company (55.5%), Charmcollection (49.0%) and LG (46.9%) • Amazon UAE: Knorr (54.0%), Ocean Patio (50.0%) and Bikuul (48.2%) • Amazon Japan: キングジム (King Jim) (59.1%), Skylight (52.0%) and Cozyone, Hbada and Bauhutte (バウヒュッテ) (all 50.0%) • Amazon Singapore: Trademark Home (59.8%), Gaggia (54.5%) and Ely’s & Co. (46.8%)
Discounts before, during and after the event
The US retailer with the biggest overall home discount before Prime Day was Amazon US (27.0%). Amazon’s biggest pre-event discounts were on power & hand tools (28.6%), kitchen (28.3%) and furniture (28.0%).
Ace Hardware offered the biggest discounts on power & hand tools during and after the event (both 34.1%).
Amazon UK stood out for discounts this Prime Day. It was the UK retailer with the biggest overall home discount before (26.1%) Prime Day, with the deepest discounts on appliances (29.0%), power & hand tools (27.1%) and pet supplies (25.9%).
During Prime Day, Etsy and Amazon UK offered the biggest discounts (29.7% and 29.6%, respectively). Etsy’s top discounts were on pet supplies (40.0%), kitchen (32.5%) and bed & bath (28.1%), while Amazon UK’s top discounts were on power & hand tools (32.3%), appliances (31.4%) and pet supplies (28.9%).
After the event, Etsy had the biggest discount (29.8%), led by kitchen (34.3%), pet supplies (32.9%) and bed & bath (28.9%).
In Europe, Amazon Italy offered the biggest overall home discount before (31.6%) and during (29.4%) Prime Day. Amazon France offered the biggest discount after (21.4%) Prime Day.
In the pre-sales event, Amazon Italy gave the most generous discounts on pet supplies (31.6%) and appliances (9.3%).
During Prime Day, Amazon Italy offered the biggest discounts on pet supplies (31.6%), furniture (28.3%) and appliances (9.3%).
After Prime Day, Amazon France offered the biggest discounts on kitchen (24.6%), appliances (22.9%) and pet supplies (21.4%).
Popularity
In the US, among home products with high popularity, Amazon US offered the highest percentage of items with a price decrease (26.8%) and Target offered the greatest magnitude of price decrease (23.6%).
For home items with moderate popularity, Amazon US offered the highest percentage of items with a price decrease (26.9%) and Target offered the greatest magnitude of price decrease (18.9%).
Among home merchandise with low popularity, Amazon US offered both the highest percentage of items with a price decrease (23.8%) and the greatest magnitude of price decrease (15.9%).
Amazon UK stood out in this analysis of product popularity. In the UK, among home products with high popularity, Amazon UK offered the highest percentage of items with a price decrease (37.1%) and Etsy offered the greatest magnitude of price decrease (20.9%).
For home items with medium popularity, Amazon UK offered the highest percentage of items with a price decrease (35.9%) and Etsy offered the greatest magnitude of price decrease (24.4%).
Among home merchandise with low popularity, Amazon UK offered both the highest percentage of items with a price decrease (34.9%) and the greatest magnitude of price decrease (21.7%).
In Europe, Amazon Germany stood out for discounts for home products across all levels of popularity.
Among home goods with high popularity, Amazon Germany offered both the highest overall percentage of items with a price decrease (29.1%) and the greatest overall magnitude of price decrease (19.1%).
For home items with medium popularity, Amazon Germany offered both the highest percentage of items with a price decrease (28.4%) and the greatest magnitude of price decrease (19.8%).
Among home merchandise with low popularity, Amazon Germany offered the highest percentage of items with a price decrease (22.5%) and Amazon Italy offered the greatest magnitude of price decrease (55.1%) related to a product count of 9.
In Middle East & Asia, among home items with high popularity, Amazon Singapore offered the highest overall percentage of items with a price decrease (35.6%) and Amazon Saudi Arabia had the greatest overall magnitude of price decrease (19.4%).
For home products with medium popularity, Amazon UAE offered both the highest percentage of items with a price decrease (47.5%) and the greatest magnitude of price decrease (16.0%).
Among home goods with low popularity, Amazon UAE offered the highest percentage of items with a price decrease (43.3%) and Amazon Japan had the greatest magnitude of price decrease (15.5%).
Prime Day 2021 hit a global home run
Overall, Prime Day 2021 offered consumers many generous deals on home products across every region.
According to our analysis, the retailers whose Prime Day pricing stood the most were Amazon US and Target in the US, Amazon UK and Etsy in the UK, Amazon Germany and Amazon Italy in Europe, Amazon UAE in the Middle East and Amazon Japan in Asia.
Consumers are moving beyond yoga pants. After a long year of pandemic lockdowns and the casual comfort of a homebody lifestyle, shoppers are giving their wardrobes a makeover. During this year’s Prime Day sales event, retailers around the world were well prepared for fashion shoppers’ enthusiasm for a fresh look.
That’s why we at DataWeave wanted to know how Prime Day 2021 discounts played a role in fashion marketing. We focused our analysis on how global retailers adapted their Prime Day pricing strategies to distinguish their offerings across seven fashion subcategories, including men’s and women’s shoes, and clothing & accessories.
Our Methodology We tracked the pricing of products among 16 leading retailers in nine countries across five regions, including:
• The US (Amazon US, Nordstrom, Target, Walmart and Zappos) • The UK (Amazon UK, Ebay, Etsy and OnBuy) • Europe (Amazon France, Amazon Germany and Amazon Italy) • The Middle East (Amazon Saudi Arabia and Amazon UAE) • Asia (Amazon Japan and Amazon Singapore)
This year’s results showed some impressive discounts among retailers and across regions. Let’s see how retailers used pricing as a competitive strategy to win Prime Day sales in the fashion category, as well as international fashion brands that stood out for the generous discounts on their products.
Percentage of items with a price decrease
The US retailer with the overall highest percentage of fashion products with a price decrease for Prime Day was Amazon US (34.5%).
Fashion subcategories with the highest percentage of items with a price decrease per US retailer were:
Brands with the greatest magnitude of price decreases on fashions per US retailer included:
• Amazon US: Free Soldier (57.3%), Rockport (52.0%) and Alvaq (48.9%) • Nordstrom: Little Words Project (60.0%), Robert Barakett and Nordstrom (50.0%) and Bonobos (49.1%) • Target: Cowboy Bebop, Avatar: The Last Airbender, YuYu Hakusho, Dragon Ball Z and Naruto (all 40.0%) • Walmart: Tredsafe (42.2%), C. Wonder and Crocs (both 30.0%) and Deer Stags (26.1%) • Zappos: Volcom (25.9%), O’Neill (20.8%) and Bandolino (20.5%)
In the UK, Ebay and Etsy tied for the greatest overall magnitude of price decrease on fashion products for Prime Day (both 14.7%).
The fashion subcategories with the greatest magnitude of price decrease per UK retailer were:
Brands with the greatest magnitude of price decreases per retailer in the Middle East and Asia included:
• Amazon Saudi Arabia: Dorina (48.7%), Cole Haan (40.2%) and Boss (39.7%) • Amazon UAE: Aldo (53.2%), Mvmt (51.4%) and Inkast Denim Co. (39.4%) • Amazon Japan: Face Trick Glasses (30.2%), モアプレッシャー (More Pressure) (23.7%) and アツギ (Atsugi) (21.7%) • Amazon Singapore: Bloch (49.0%), Adidas (38.5%) and Chums (31.4%)
Discounts before, during and after the event
Nordstrom was the US retailer with the biggest overall fashion discount before (39.0%), during (40.5%) and after (41.2%) Prime Day.
Nordstrom’s biggest pre-event discounts were on women’s shoes (44.2%), women’s clothing & accessories (36.6%) and men’s clothing & accessories (36.1%).
Women’s shoes (43.1%), men’s shoes (42.6%) and men’s clothing & accessories (39.5%) were the leading subcategories for Nordstrom’s discounts during Prime Day.
After the event, Nordstrom’s biggest discounts were for women’s shoes (42.5%), men’s shoes (42.2%) and men’s clothing & accessories (41.5%).
In the UK, Ebay offered the highest discounts before (43.7%), during (42.1%) and after (42.3%) Prime Day.
Before Prime Day, Ebay biggest discounts were on men’s clothing & accessories (47.9%), men’s shoes (43.9%) and watches (43.3%).
Ebay’s top discounts during Prime Day were on men’s clothing & accessories (45.3%), men’s shoes (44.6%) and women’s shoes (39.5%).
After the event, Ebay had the biggest discounts on men’s clothing & accessories (45.6%), women’s shoes (42.9%) and men’s shoes (41.9%).
In Europe, Amazon Germany dominated with the biggest overall fashion discounts before (27.1%), during (31.8%) and after (26.5%) Prime Day.
In the pre-sales event, Amazon Germany offered its most generous discounts on watches (27.5%), women’s shoes (12.6%) and men’s shoes (5.7%).
During Prime Day, Amazon Germany’s biggest discounts were on women’s clothing & accessories (36.8%), men’s clothing & accessories (32.5%) and watches (32.0%).
After Prime Day, Amazon Germany had the highest discounts on women’s clothing & accessories (34.1%), men’s clothing & accessories (27.6%) and watches (26.6%).
Popularity
In the US, among fashion products with high popularity, Amazon US offered the highest percentage of items with a price decrease (36.8%) and Nordstrom offered the greatest magnitude of price decrease (29.8%).
For fashion items with medium popularity, Amazon US offered the highest percentage of items with a price decrease (35.2%), and Nordstrom offered the greatest magnitude of price decrease (28.1%).
Among fashion merchandise with low popularity, Amazon US offered the highest percentage of items with a price decrease (27.2%) and Nordstrom offered the greatest magnitude of price decrease (31.6%).
In the UK, among fashion products with high popularity, Amazon UK offered the highest percentage of items with a price decrease (31.6%) and Ebay offered the greatest magnitude of price decrease (14.3%).
For fashion items with medium popularity, Amazon UK offered the highest percentage of items with a price decrease (32.5%) and Ebay offered the greatest magnitude of price decrease (17.6%).
Among fashion merchandise with low popularity, Amazon UK offered the highest percentage of items with a price decrease (24.5%) and Etsy offered the greatest magnitude of price decrease (23.1%).
In Europe, Amazon Germany dominated discounts for Prime Day 2021 across all levels of popularity.
Among fashion goods with high popularity, Amazon Germany offered both the highest overall percentage of items with a price decrease (40.5%) and the greatest overall magnitude of price decrease (16.3%).
For fashion items with medium popularity, Amazon Germany offered both the highest percentage of items with a price decrease (32.5%) and the greatest magnitude of price decrease (16.5%).
Among fashion merchandise with low popularity, Amazon Germany offered the highest percentage of items with a price decrease (30.8%) and the greatest magnitude of price decrease (15.1%).
In Middle East & Asia, among fashion items with high popularity, Amazon Saudi Arabia offered the highest overall percentage of items with a price decrease (55.9%) and Amazon UAE had the greatest overall magnitude of price decrease (20.6%).
For fashion products with medium popularity, Amazon UAE offered both the highest overall percentage of items with a price decrease (49.3%) and the greatest overall magnitude of price decrease (17.8%).
Among fashion items with low popularity, Amazon UAE offered the highest percentage of items with a price decrease (46.1%) and Amazon Japan had the greatest magnitude of price decrease (21.5%).
Prime Day fashion deals galore
Overall, Prime Day 2021 gave global shoppers an abundance of generous discounts on fashion items.
According to our analysis, the retailers whose Prime Day pricing stood out the most were Amazon US and Nordstrom in the US, Amazon UK and Ebay in the UK, Amazon Germany in Europe, Amazon UAE in the Middle East and Amazon Japan in Asia.
As consumers socialize more this year, retailers around the world are competing for sales in the torrid health & beauty category.
That’s why we at DataWeave wanted to know how Prime Day 2021 discounts played a role in the pricing strategies for health & beauty products. We focused our analysis on how global retailers adapted their Prime Day pricing strategies to distinguish their offerings across seven health & beauty subcategories, including makeup, health care and baby care.
Our Methodology We tracked the pricing of products among 16 leading retailers in nine countries across five regions, including:
● The US (Amazon US, Sephora, Target, Ulta and Walmart) ● The UK (Amazon UK, Ebay, Etsy and OnBuy) ● Europe (Amazon France, Amazon Germany and Amazon Italy) ● The Middle East (Amazon Saudi Arabia and Amazon UAE) ● Asia (Amazon Japan and Amazon Singapore)
The results showed some surprising differences among retailers and regions. See how retailers used pricing as a competitive strategy to win Prime Day sales in the health & beauty category, as well as international health & beauty brands that stood out for the discounts on their products.
Percentage of items with a price decrease
The US retailer with the overall highest percentage of health & beauty products with a price decrease for Prime Day was Amazon US (23.9%).
Health & beauty subcategories with the highest percentage of items with a price decrease per US retailer were:
● Amazon US: Fragrance (32.4%), oral care (27.1%) and skin care (22.7%) ● Sephora: Makeup (0.2%) ● Target: Oral care (2.7%), baby care (1.6%) and hair care (0.8%) ● Ulta: Makeup (3.7%), skin care (0.8%) and hair care (0.1%) ● Walmart: Fragrance (35.3%), hair care (27.0%) and skin care (23.1%)
By far, the UK retailer with the overall highest percentage of items with a price decrease for Prime Day was Amazon UK (41.9%).
Health & beauty subcategories with the highest percentage of items with a price decrease per UK retailer were:
● Amazon UK: Oral care (62.5%), fragrance (46.2%) and hair care and skin care (both 45.4%) ● Ebay: Makeup (8.1%), fragrance (7.1%) and skin care (4.7%) ● Etsy: Oral care (3.4%), skin care (2.3%) and makeup (0.9%)
In Europe, Amazon Germany had the overall highest percentage of items with a price decrease for Prime Day (33.1%).
Health & beauty subcategories with the highest percentage of items with a price decrease per European retailer were:
● Amazon France: Skin care (27.2%), fragrance (25.0%) and hair care (24.4%) ● Amazon Germany: Skin care (49.7%), fragrance (41.3%) and hair care (37.2%) ● Amazon Italy: Skin care (5.9%)
Across the Middle East & Asia, Amazon UAE had the overall highest percentage of health & beauty items with a price decrease for Prime Day (54.1%).
Health & beauty subcategories with the highest percentage of items with a price decrease per retailer were:
● Amazon Saudi Arabia: Health care (56.4%), hair care (48.8%) and fragrance (46.2%) ● Amazon UAE: Skin care (64.3%), fragrance (64.0%), and hair care (58.4%) ● Amazon Japan: Oral care (5.3%), skin care (4.5%) and makeup (3.3%) ● Amazon Singapore: Baby care and health care (both 35.6%), makeup (32.6%) and fragrance (31.3%)
Magnitude of price decrease
The US retailer with the greatest overall magnitude of price decrease for Prime Day was Ulta (33.3%).
The health & beauty subcategories with the greatest magnitude of price decrease per US retailer were:
● Amazon US: Hair care (18.0%), baby care (15.5%) and health care (15.4%) ● Sephora: Makeup (24.5%) ● Target: Hair care (46.6%), oral care (28.4%) and skin care and baby care (both 15.0%) ● Ulta: Hair care (40.8%), skin care (34.0%) and makeup (32.5%) ● Walmart: Baby care (11.5%), skin care (11.3%) and hair care (11.0%)
Brands with the greatest magnitude of price decreases per US retailer included:
● Amazon US: Cerave (54.5%), Aquaphor (54.4%) and Yankee Candle (50.7%) ● Sephora: Nars (25.9%) and Marc Jacobs Beauty (23.1%) ● Target: Kristin Ess (50.0%), Hot Tools (48.6%) and Arc Oral Care (both 40.1%) ● Ulta: KKW Beauty, Lime Crime, Ulta and NYX Professional Makeup (all 50.0%), CoverGirl (47.8%) and Biolage (40.8%) ● Walmart: Whitening Toothpaste (57.6%), Absolute New York (56.7%) and Zdmathe (48.5%)
The UK retailer with the greatest overall magnitude of price decrease on health & beauty products for Prime Day was Amazon UK (18.6%).
The health & beauty subcategories with the greatest magnitude of price decrease per UK retailer were:
● Amazon UK: Oral care (23.5%), makeup (22.0%) and skin care (20.2%) ● Ebay: Hair care (16.0%), fragrance (14.4%) and makeup (11.5%) ● Etsy: Makeup (20.0%), oral care (16.1%) and skin care (13.3%)
Brands with the greatest magnitude of price decreases across health & beauty sub-categories per UK retailer included:
● Amazon UK: Philips Sonicair (56.8%), BaByliss For Men (53.0%) and Dr. PawPaw (52.9%) ● Ebay: Oral-B Braun (50.3%), Clean (50.1%) and Versace (46.0%) ● Etsy: Valdenize (both 48.4%), Allure Wedding Jewelry (32.8%) and Moroccan White (30.0%)
Among European retailers, Amazon Germany offered the greatest overall magnitude of price decrease on health & beauty products for Prime Day (20.2%).
The health & beauty subcategories with the greatest magnitude of price decrease per European retailer were:
● Amazon France: Skin care (20.4%), baby care (17.8%) and makeup (16.2%) ● Amazon Germany: Skin care (28.2%), makeup (22.6%) and health care (21.3%) ● Amazon Italy: Skin care (9.9%)
Brands with the greatest magnitude of price decreases per European retailer included:
● Amazon France: Look Concept (59.8%), Douyao (57.5%) and Eco Styler (57.4% for both hair care and health care) ● Amazon Germany: Le Cuisinier (58.4%), Beurer (47.5%) and Solida (45.9%) ● Amazon Italy: Bezox (9.9%)
Across the Middle East and Asia, Amazon Japan offered the greatest overall magnitude of price decrease on health & beauty products for Prime Day (18.0%).
The health & beauty subcategories with the greatest magnitude of price decrease per retailer were:
● Amazon Saudi Arabia: Health care (25.1%), skin care (19.3%) and baby care (16.5%) ● Amazon UAE: Makeup (23.1%), hair care (18.3%) and baby care (18.1%) ● Amazon Japan: Skin care (27.6%), hair care (17.2%) and makeup (14.7%) ● Amazon Singapore: Skin care (10.1%), hair care (8.9%) and health care (8.2%)
Brands with the greatest magnitude of price decreases per retailer in the Middle East and Asia included:
● Amazon Saudi Arabia: Tide (59.8%), bblüv (58.3%) and Mas (55.0%) ● Amazon UAE: Syoss (59.7%), Vertex (59.1%) and Onesea (58.7%) ● Amazon Japan: ドクターブロナー (Dr. Bronner’s) (54.8%), ゼルマ (Zelma) (50.0%) and いち髪 (47.8%) ● Amazon Singapore: Changing Lifestyles (56.6%), Dynarex (53.0%) and Grohe (52.5%)
Discounts before, during and after the event
In the US, specialty beauty retailers’ discounts stood out during Prime Day sales. The US retailer with the biggest overall health & beauty discount before (43.4%), during (39.3%) and after (39.4%) Prime Day was Ulta. During and after Prime Day, Sephora was a close second at 38.2% for both periods.
Ulta’s biggest pre-event discounts were on makeup (44.2%) and skin care (33.0%). Hair care (40.8%). makeup (39.5%) and skin care (36.0%) were the leading subcategories for Ulta’s discounts during Prime Day. After the event, Ulta’s biggest discounts were for hair care (40.8%), makeup (39.6%) and skin care (37.3%)
In the UK, OnBuy offered the highest discounts before, during and after Prime Day at 70.0% off baby care products. Yet the total product count was only 2.
Among the remaining rivals, all of whom had a product count above 1000, Ebay had the highest discounts before (30.8%), during (33.8%) and after (35.0%) Prime Day.
Before Prime Day, Ebay biggest discounts were on hair care (48.9%), fragrance (23.9%) and makeup (23.4%). Ebay’s top discounts during Prime Day were on hair care (50.3%), makeup (24.9%) and fragrance (24.8%). Similarly, after the event, Ebay had the biggest discounts on hair care (49.9%), makeup (26.7%) and fragrance (26.2%).
Across retailers in the Middle East & Asia, Amazon UAE offered the biggest overall health & beauty discounts before (26.0%), during (30.7%) and after (26.0%) Prime Day.
In the pre-sales event, Amazon UAE offered the most generous discounts on makeup (30.7%), fragrance (29.9%) and health care (29.2%).
During Prime Day, Amazon UAE’s biggest discounts were on makeup (37.2%), fragrance (31.6%) and health care (31.3%).
During Prime Day, Amazon UAE offered the biggest discounts on fragrance (30.5%), makeup (30.0%) and health care and baby care (both 26.8%).
Popularity
In the US, among health & beauty products with high popularity, Amazon US offered the highest percentage of items with a price decrease (24.3%) and Target offered the greatest magnitude of price decrease (29.3%).
For health & beauty items with medium popularity, Amazon US offered the highest percentage of items with a price decrease (26.0%), and strategic partners Target and Ulta both offered the greatest magnitude of price decrease (33.4%).
Among health & beauty merchandise with low popularity, Walmart offered the highest percentage of items with a price decrease (18.5%) and Ulta offered the greatest magnitude of price decrease (37.4%).
Amazon UK stood out among all levels of health & beauty product popularity.
In the UK, among health & beauty products with high popularity, Amazon UK offered both the highest percentage of items with a price decrease (42.8%) and the greatest magnitude of price decrease (18.6%).
For health & beauty items with medium popularity, Amazon UK offered the highest percentage of items with a price decrease (42.8%) and Etsy offered the greatest magnitude of price decrease (20.1%).
Among health & beauty merchandise with low popularity, Amazon UK offered both the highest percentage of items with a price decrease (35.4%) and the greatest magnitude of price decrease (17.6%).
In Europe, Amazon Germany dominated discounts for health & beauty products across all levels of popularity.
Among health & beauty goods with high popularity, Amazon Germany offered both the highest overall percentage of items with a price decrease (34.6%) and the greatest overall magnitude of price decrease (20.6%).
For health & beauty items with medium popularity, Amazon Germany offered both the highest percentage of items with a price decrease (32.7%) and the greatest magnitude of price decrease (20.0%).
Among health & beauty merchandise with low popularity, Amazon Germany offered the highest percentage of items with a price decrease (33.5%) and the greatest magnitude of price decrease (20.6%).
In Middle East & Asia, among health & beauty items with high popularity, Amazon UAE offered the highest overall percentage of items with a price decrease (53.0%) and Amazon Saudi Arabia had the greatest overall magnitude of price decrease (18.7%).
For health & beauty products with medium popularity, Amazon UAE offered the highest overall percentage of items with a price decrease (55.3%) and Amazon Saudi Arabia had the greatest overall magnitude of price decrease (19.7%).
Among health & beauty goods with low popularity, Amazon UAE offered the highest percentage of items with a price decrease (54.5%) and Amazon Japan had the greatest magnitude of price decrease (18.7%).
Health & beauty’s stunning Prime Day deals
Overall, Prime Day 2021 gave shoppers around the world the opportunity to score generous discounts on health & beauty products.
According to our analysis, the retailers whose Prime Day pricing stood out the most were Amazon US and Ulta in the US, Amazon UK and Ebay in the UK, Amazon Germany in Europe, Amazon UAE in the Middle East and Amazon Japan in Asia.
Stay tuned for Prime Day 2021 international fashion and home goods pricing insights.
Amazon’s Prime Day 2021 global shopping event took place June 21 and 22, 2021 and smashed previous sales records. At DataWeave, we wanted to know how Prime Day 2021 deals and discounts on electronics compared across retailers and regions. We focused on how retailers adapted their Prime Day pricing strategies to stand out in the competitive consumer electronics category.
Our Methodology We tracked the pricing of several leading retailers in nine countries across five regions, including:
• The US (Amazon US, Best Buy, Target and Walmart) • The UK (Amazon UK, Ebay, Etsy and OnBuy) • Europe (Amazon France, Amazon Germany and Amazon Italy) • The Middle East (Amazon Saudi Arabia and Amazon UAE) • Asia (Amazon Japan and Amazon Singapore)
Let’s see how retailers used pricing tactics to gain a competitive advantage during Prime Day, as well as which electronics brands had the highest discounts around the world.
Percentage of items with a price decrease
The US retailer with the overall highest percentage of items with a price decrease for Prime Day was Amazon US (26.3%).
Electronics subcategories with the highest percentage of items with a price decrease per US retailer were:
• Amazon US: Headphones (36.3%), video games and electronics (both 35.8%) and cameras (35.7%) • Best Buy: Wearables (10.1%) and electronics (6.8%) • Target: Electronics (21.4%), Bluetooth & wireless speakers (19.4%) and tech accessories (14.8%) • Walmart: Cell phones & accessories (25.8%), TV & video (14.9%) and cameras (9.4%)
The UK retailer with the overall highest percentage of items with a price decrease for Prime Day was Amazon UK (30.4%), closely followed by OnBuy (30.0%). Of note, Amazon UK offered price increases on 10 times as many products as OnBuy (2379 vs. 237).
Electronics subcategories with the highest percentage of items with a price decrease per UK retailer were:
• Amazon UK: Electronics (53.2%), cameras (41.8%) and headphones (41.5%) • Ebay: TV & video (19.2%), computers & office (12.2%) and cell phones & accessories (7.5%) • Etsy: Electronics (1.4%) • OnBuy: Cell phones & accessories (35.5%) and wearables (4.8%)
In Europe, Amazon Germany had the overall highest percentage of items with a price decrease for Prime Day (28.9%).
Electronics subcategories with the highest percentage of items with a price decrease per European retailer were:
Across the Middle East & Asia, Amazon UAE had the overall highest percentage of items with a price decrease for Prime Day (36.1%).
Electronics subcategories with the highest percentage of items with a price decrease per retailer were:
• Amazon Saudi Arabia: Video games (47.8%), electronics (46.8%) and cell phones & accessories (41.2%) • Amazon UAE: Electronics (63.4%), tech accessories (60.3%) and cell phones & accessories (60.1%) • Amazon Japan: TV & video (14.9%), musical instruments (11.8%) and electronics (10.3%) • Amazon Singapore: Wearables (32.2%), car electronics (30.4%) and musical instruments (30.2%)
Magnitude of price decrease
The US retailer with the greatest overall magnitude of price decrease for Prime Day was Target (18.6%).
The electronics subcategories with the greatest magnitude of price decrease per US retailer were:
• Amazon US: Cell phones & accessories (20.4%), electronics (20.1%) and headphones (18.7%) • Best Buy: Wearables (16.9%) and electronics (13.3%) • Target: Video games (27.9%), tech accessories (25.6%) and headphones (24.4%) • Walmart: TV & video (10.6%), computers & office (10.3%) and home audio & theater (10.1%)
Brands with the greatest magnitude of price decreases per US retailer included:
• Amazon US: JBL (57.2%), Hape (55.0%) and Falcon (52.8%) • Best Buy: Bower (40.0%), Vtech (36.7%) and Wingthings (30.0%) • Target: 2k Sports and 2K Games (both 56.7%), Little Tikes (50.0%) • Walmart: Moonlite (54.3%), Sceptre (45.9%) and Polaroid (38.5%)
The UK retailer with the greatest overall magnitude of price decrease for Prime Day was OnBuy (22.1%).
The electronics subcategories with the greatest magnitude of price decrease per UK retailer were:
• Amazon UK: Home audio & theater (28.4%), electronics (21.7%) and cell phones & accessories (20.1%) • Ebay: TV & video (19.5%), wearables (18.7%) and computer & office (16.6%) • Etsy: Electronics (14.8%) • OnBuy: Cell phones & accessories (22.3%) and wearables (5.9%)
Brands with the greatest magnitude of price decreases across electronics categories per UK retailer included:
• Amazon UK: Amazon (58.3% for both cell phones & accessories and headphones), Flexson (55.4%) and Ibra (47.9% for both cameras and TV & video) • Ebay: Falcon (52.0%), Ticwatch (48.4%) and Grougs by Live Lead (47.9%) • OnBuy: Sony (33.1%), Apple (23.1%) and Samsung (21.3%)
Among European retailers, Amazon Italy offered the greatest overall magnitude of price decrease for Prime Day (18.9%) among a total of 66 products.
The electronics subcategories with the greatest magnitude of price decrease per European retailer were:
• Amazon France: Home audio & theater (13.1%), video games (10.5%) and TV & video (10.3%) • Amazon Germany: Video games (22.0%), electronics and musical instruments (both 20.3%) and cell phones & accessories (20.2%) • Amazon Italy: Cell phones & accessories (41.2%) and headphones (28.1%)
Brands with the greatest magnitude of price decreases per European retailer included:
• Amazon France: DCSk (59.0%), Amazon Basics (58.7%) and Qoosea (46.3%) • Amazon Germany: Meister (53.5%), Rampow (51.4%) and Gewa (51.1%) • Amazon Italy: Homscam (41.2%) and Gamurry (15.1)
Across the Middle East and Asia, Amazon Japan offered the greatest overall magnitude of price decrease for Prime Day (11.9%) among a total of 66 products.
The electronics subcategories with the greatest magnitude of price decrease per retailer were:
• Amazon Saudi Arabia: Video games (16.8%), electronics (12.8%) and cell phones & accessories (12.4%) • Amazon UAE: Car electronics, headphones and tech accessories (all 13.1%) • Amazon Japan: Headphones (25.4%), home audio & theater (23.6%) and cameras (14.9%) • Amazon Singapore: Car electronics (9.4%), cell phones & accessories (8.6%) and electronics (8.3%)
Brands with the greatest magnitude of price decreases per retailer in the Middle East and Asia included:
• Amazon Saudi Arabia: Belkin (52.9%), Topoint (49.3%) and Promate (45.7%) • Amazon UAE: Amazon Basics (56.3%), Bettyliss (52.1%) and Acreate (50.3%) • Amazon Japan: タニタ(Tanita) (49.7%), Laza-Vally (47.8%) and Nebula (33.3%) • Amazon Singapore: Pintech Percussion (55.0%), Pac (53.4%) and Goldwood Sound Inc. (52.9%)
Discounts before, during and after the event
The US retailer with the biggest overall electronics discount before Prime Day was Amazon US (26.6%). Amazon’s biggest discounts were on home audio & theater (30.5%), TV & video (29.1%) and cell phones & accessories (28.6%).
Walmart offered the biggest discounts during (32.1%) and after (31.5%) the event. During the event, Walmart’s biggest discounts were on cell phones & accessories (46.3%), home audio & theater (35.5%) and computers & office (31.0%). Similarly, after the event, Walmart’s biggest discounts were on cell phones & accessories (45.8%), home audio & theater (35.3%) and computers & office (30.8%).
OnBuy was the UK retailer with the biggest overall electronics discount before (65.3%), during (68.6%) and after (69.1%) Prime Day with a product count of 237. OnBuy’s biggest discounts were on cameras (69.5% before, during and after the sales event), cell phones & accessories (rising from 67.6% before the sales event to 71.6% during and 71.8% after the event) and wearables (65.2% before and after the event yet 35.5% during Prime Day).
In Europe, Amazon Germany offered the biggest overall electronics discount before (21.4%), during (25.6%) and after (20.2%) Prime Day. Amazon France and Amazon Italy also offered comparable overall discounts (21.1%) before Prime Day.
In the pre-sales event, Amazon Germany gave the most generous discounts on cameras (34.1%), wearables (24.7%) and headphones (24.3%). During Prime Day, Amazon Germany offered the biggest discounts on video games (30.7%), headphones (30.1%) and electronics (28.3%). After Prime Day, Amazon Germany offered the biggest discounts on headphones (24.2%) electronics (22.6%) and cell phones & accessories (22.1%).
Across retailers in the Middle East & Asia, Amazon UAE offered the biggest overall electronics discount before Prime Day (24.3%), whereas Amazon Japan offered the biggest discount during (32.0%) and after (32.3%) Prime Day.
In the pre-sales event, Amazon UAE offered the most generous discounts on TV & video (31.3%), musical instruments (31.0%) and headphones (25.4%). During and after Prime Day, Amazon Japan offered the biggest discounts on Bluetooth & wireless speakers and electronics (both 99.0%).
Popularity
In the US, among electronics with high popularity, Amazon US offered the highest percentage of items with a price decrease (27.8%) and Target offered the greatest magnitude of price decrease (21.3%).
For electronics with moderate popularity, Amazon US offered the highest percentage of items with a price decrease (24.7%) and Best Buy offered the greatest magnitude of price decrease (15.4%).
Among electronics with low popularity, Amazon US offered the highest percentage of items with a price decrease (25.2%) and Best Buy offered the greatest magnitude of price decrease (13.4%), closely followed by Amazon US (13.3%).
In the UK, among electronics with high popularity, OnBuy offered the highest percentage of items with a price decrease (44.0%) among 84 products and Etsy offered the greatest magnitude of price decrease (28.5%) among 150 products.
For electronics with moderate popularity, Amazon UK offered the highest percentage of items with a price decrease (29.3%) and OnBuy offered the greatest magnitude of price decrease (26.7%).
Electronics with low popularity, Amazon UK offered the highest percentage of items with a price decrease (23.9%) and OnBuy offered the greatest magnitude of price decrease (26.3%).
In Europe, among electronics with high popularity, Amazon Germany offered the highest overall percentage of items with a price decrease (30.1%) and the greatest overall magnitude of price decrease (20.2%).
For electronics with moderate popularity, Amazon Germany offered the highest percentage of items with a price decrease (29.7%) and Amazon Italy offered the greatest magnitude of price decrease (41.2%) among 12 products.
Among electronics with low popularity, Amazon Germany offered the highest percentage of items with a price decrease (26.7%) and the greatest magnitude of price decrease (17.2%).
In Middle East & Asia, among electronics with high popularity, Amazon Singapore offered the highest overall percentage of items with a price decrease (32.2%) and Amazon Saudi Arabia had the greatest overall magnitude of price decrease (10.4%).
For electronics with moderate popularity, Amazon UAE offered the highest percentage of items with a price decrease (48.8%) and Amazon Japan offered the greatest magnitude of price decrease (11.5%).
Similarly, among electronics with low popularity, Amazon UAE offered the highest percentage of items with a price decrease (36.9%) and Amazon Japan the greatest magnitude of price decrease (13.3%).
Consumers won big on Prime Day 2021
Overall, Prime Day 2021 offered a wide range of deals across the competitive electronics category in each region. Almost all of the retailers we studied (except for Ebay) showed up in the analysis for offering notable discounts and pricing strategies this year. Amazon US, OnBuy, Amazon Germany, Amazon Japan and Amazon UAE appeared in the results most often among their respective regions. Stay tuned for Prime Day 2021 pricing insights across other categories, including home, health & beauty and fashion.
This year, shoppers will skip neckties and celebrate Dad with gifts for his home office or man cave. As our personal and professional lives grow increasingly digital and tied to our homes, retailers face new seasonal sales opportunities.Retailers whose assortments contain in-demand electronics and home products can drive more e-commerce sales revenue and gain a competitive edge in time for Father’s Day 2021.
According to the NRF, Father’s Day spending is expected to hit $20.1 billion, up 18% from 2020’s total of $17 billion. The vast majority (75%) of Americans plan to celebrate the fathers, husbands and other paternal figures in their life this Father’s Day. Popular products dads will love
Retailers can inspire Father’s Day shoppers by filling their assortments with in-demand electronics and home products, as these two categories continue to boom.
Consider these recent results related to electronics and home goods:
Online sales of consumer electronics grew 18% year-over-year in 2020 as more consumers work, shop and enjoy entertainment in the comfort of their homes.
To win more sales on Black Friday 2020, certain retailers offered attractive deals and deep discounts on electronics like laptops, mobiles, wearables, USB flash drives, tablets and headphones.
Home furnishings sales rose 12% year-over-year in 2020 as homebound consumers invested in products for domestic comfort, organization and functional purposes.
On Cyber Monday 2020, home merchandise saw bustling sales, as storage items, cabinets and bookcases were among the most competitively priced products in the category.
Since home is the new hub, retailers can plan their assortments to align with this enduring consumer trend to outplay rivals. Optimizing their product mix involves making decisions on the right balance among bestsellers, hot trends, unique products and essential items to gain a competitive advantage.
Grab shoppers’ attention with desirable promotions
Although shoppers appreciate variety, the abundance of product choices available online can overwhelm consumers. In response, retailers can craft persuasive and timely digital campaigns to help simplify the customer experience.
Digital promotions, including banner ads and search campaigns, can help retailers spark a sense of urgency that motivates shoppers to buy. The key is for retailers to connect to consumers with the right messaging, timing and targeting to earn their attention, trust and sales. Retailers need effective promotions to optimize their ad spend. Pricing secures the sale
To maximize top line performance, retailers also need to nail their Father’s Day pricing strategies.
Notably, consumption habits and loyalty have dramatically shifted during the pandemic, which has affected retailers’ pricing strategies. Value pricing continues to soar due to economic uncertainty, job losses and a growing desire for value for money. Last year, 30% of consumers switched to a new brand due to better prices, while 25% cited better value as the reason they switched, according to McKinsey & Company.
On the other side of the socioeconomic spectrum, premium pricing is also on the rise. Upscale shoppers are now more willing to splurge on high quality goods, including home furnishings and electronics. These consumers will pay more for merchandise that adds value or purpose to their lives. In addition, digitally-savvy Gen Z and Millennial consumers are spending 125% as much as they did in 2019. As a result, retailers that capitalize on consumers’ enthusiasm and price elasticity will drive incremental e-commerce revenue gains.
As e-commerce competition intensifies and informed, empowered shoppers know where to find the best prices, more retailers now seek a new pricing approach to stand out, drive sales growth and protect against price wars. Drive revenue with the right products, promotions and prices
To win the attention and sales of Father’s Day shoppers, more leading retailers now use data insights to make faster, more effective assortment and pricing decisions that maximize their e-commerce sales.
Data analytics help retailers know which products consumers will actually buy. Leading global retailers rely on Assortment Analytics from DataWeave to ensure their online assortments keep up with evolving consumer needs. Building acompetitive product mix can set retailers apart and boost e-commerce sales by offering in-demand merchandise. Assortment analytics give retailers insights on the most popular brands and products on any e-commerce website, and help them spot and fill any assortment gaps to capture more sales.
To captivate online shoppers’ attention, retailers use DataWeave’s Promotional Insights to lower acquisition costs with marketing promotions that resonate. As online shoppers increasingly seek timely offers, these insights help retailers quickly evaluate the effectiveness of their promotions and optimize their digital ad spend. Retailers gain near-real-time insights on the brands, categories and products their rivals promote, including campaign frequency, duration and messaging for promotions that convert.
Major retailers also turn to Pricing Intelligence from DataWeave to promptly adapt to rivals’ online price changes and shifts in consumer demand. Retailers drive more revenue and margin by easily identifying fluctuations in consumer demand and rivals’ pricing, as well as any gaps. Retailers gain an edge by seeing pricing patterns that their rivals miss. They gain accurate exact and similar product matching, and near real-time pricing updates to stay competitive and fuel e-commerce conversions. Data insights help retailers delight dads
This Father’s Day, retailers can apply data insights to offer consumers eye-catching promotions of in-demand electronics and home products at the right price to wow dads. Insights from DataWeave can help retailers make smart, competitive assortment, promotion and pricing decisions that boost agility, improve the customer experience and drive e-commerce sales for this special occasion – and all year long.
The pandemic’s profound economic impact sparked a surge in value shopping.
Between February and December 2020, 10 million Americans lost their jobs.1 Due to the pandemic, 36% of lower-income adults and 28% of middle-income adults lost a job or took a pay cut (vs. 22% of upper-income adults). In addition, less than a quarter of lower-income adults have three months’ worth of emergency funds (vs. 48% of middle-income adults and 75% of upper-income adults).2
These financial shifts matter to retailers, as lower- and middle-income households account for 81% (29% and 52%, respectively) of the total U.S. population.3 Reduced disposable income among households like these has led more consumers to embrace bargain-hunting as a shopping habit.
We’ll see why price sensitive consumers are influencing retailers to adjust their e-commerce pricing strategies to stay competitive and responsive.
Consumers seek value across retail categories
Recent research shows 50% of U.S. adults are more sensitive to product prices now than before the pandemic. Also, 80% of U.S. shoppers are taking at least one action to seek more value when they shop for groceries, prioritizing value for money over speed.4
According to McKinsey, 65% of consumers cited value as one of their top three reasons they switched brands during the pandemic. Also, 40% of shoppers cited a desire for better value and 38% cited better prices or promotions as reasons for choosing new products.5
Value-oriented pricing influences purchases, as 70% of consumers said product discounts are more important today compared to a year ago. In addition, 54% ofconsumers said better online deals and discounts are a leading factor that persuades them to choose a specific retailer.6
As e-commerce explodes, consumers have greater access to information. They can find the best price across online sites and receive notifications when a product’s price drops before they buy. Retailers face intense pricing pressure
Similar to the aftermath of the 2008 recession, discounters and dollar chain retailers are now thriving as consumers seek superior value for money. Consumers need new products yet they no longer want to spend as much as before.
That’s why bargain retail is poised to be among the biggest winners in 2021 as consumers get out and socialize more. 7
Dollar General continues to aggressively expand its omnichannel reach as value shopping soars.8 To stay competitive, Family Dollar has partnered with Instacart on same-day delivery.9 In the fierce grocery sector, hard discounter Aldi’s omnichannel expansion includes a focus on private labels and efficient operational processes that improve cost effectiveness and competitive pricing.10
Across retail categories,a remarkable 50 million price changes take place online every day.Given consumers’ shift to value shopping, more retailers are changing their pricing to offer discounts both online and in-store.11 However, to avoid costly price wars, more retailers are now taking a renewed approach to their pricing strategies to protect their margins as they compete.
Specifically, to optimize their e-commerce business for profitable growth, more retailers are modernizing their pricing strategies with data insights.
Pricing intelligence is retailers’ secret weapon
As e-commerce rivalry heats up, retailers must evaluate pricing across more online websites to keep their own prices competitive. This process is becoming increasingly complex and time consuming. Meanwhile, retailers may consider adopting aggressive pricing tactics to win online sales. Yet this pricing strategy is unsustainable over the longer term, as it erodes profit margins.
Today’s heated e-commerce rivalry means retailers can no longer afford to guess at price points or use the same pricing tactics that relied on before the pandemic.
That’s why leading retailers turn to data insights for their pricing strategies to stay agile and flexible while rapidly adapting to fluctuations in consumer demand and competitors’ pricing.
Now more retailers turn to DataWeave’s Pricing Intelligence to drive more revenue and margin.
To optimize profit margins, retailers use our actionable insights to make pricing decisions according to data-driven recommendations. They also make decisions to protect their desired price perception.
Monitoring competitors’ pricing moves helps retailers benchmark their own performance, identify gaps and respond to market trends faster. They can also refer to historic pricing data analytics to accurately anticipate and counter rivals’ next moves to gain an edge.
Retailers that apply data insights to optimize their pricing can drive more online revenue by finding the ideal price consumers are willing to pay while still maintaining profitability. Pricing intelligence can make customer acquisition more efficient, and help retailers grow online sales and market share.
Amid greater price sensitivity, retailers’ pricing strategies are evolving to use data to adapt to consumers’ needs and drive e-commerce sales and profitability. DataWeave’s Pricing Intelligence gives retailers an edge so they stay agile and competitive, and maximize e-commerce sales across consumers of all economic levels.
1. Howland, Daphne. The middle class is stressed and the pandemic isn’t helping. Retail Dive. January 20, 2021. 2. Howland, Daphne. The middle class is stressed and the pandemic isn’t helping. Retail Dive. January 20, 2021. 3. Bennett, Jesse, Richard Fry and Rakesh Kochhar. Are you in the American middle class? Find out with our income calculator. Pew Research Center. July 23, 2020. 4. Maake, Katishi. DoorDash, Instacart Eye Launching Credit Cards. The Harris Poll. April 9, 2021. 5. Charm, Tamara, Harrison Gillis, Anne Grimmelt, Grace Hua, Kelsey Robinson and Ramiro Sanchez Caballero.Survey: US consumer sentiment during the coronavirus crisis. McKinsey & Company. May 13, 2021. 6. Berthiaume, Dan. Survey: Deals drive purchases during pandemic. Chain Store Age. March 18, 2021. 7. Thomas, Lauren. Beauty and bargain retail could be the biggest winners in 2021, Wells Fargo predicts. CNBC. March 25, 2021. 8. Unglesbee, Ben. Dollar General ramps up expansion of Popshelf concept. Retail Dive. March 19, 2021. 9. Ryan, Tom. Will same-day delivery pay off for dollar stores? RetailWire. February 8, 2021. 10. Anderson, George. Should Aldi’s growing store count and digital progress keep rivals up at night? RetailWire. February 11, 2021. 11. Berthiaume, Dan. Survey: Deals drive purchases during pandemic. Chain Store Age. March 18, 2021
Moms deserve a scrumptious celebration. In time for Mother’s Day, restaurants and their food delivery partners can unburden mothers from the chore of cooking by delivering the gifts of ease, convenience and nourishment.
Over the past year, moms have been starved for time amid the disruption of working from home and supporting their children’s virtual schooling. Meanwhile, grandmothers have been starved for social connection, as many of them have only seen their loved ones on Zoom.
Restaurants can satisfy consumers’ unmet needs. Using timely, empathetic digital marketing can help restaurant operators stand out on food delivery apps (like DoorDash, Uber Eats, Grubhub and Postmates) and sell more online this Mother’s Day – and all year round.
Delight moms with what they really want
According to the NRF,83% of consumers plan to celebrate Mother’s Day in 2021. On average, shoppers plan to spend $220.48 (up $16 since last year), the highest amount in the history of NRF’s Mother’s Day surveys. 1
Most (62%) moms say they would love to eliminate the chore of cooking on Mother’s Day. Dinner is the most important meal on Mother’s Day, and most moms prefer restaurant meals (53%) to home cooked meals (39%). 2
Given consumers’ willingness to spend and Mom’s appetite for restaurants, Mother’s Day 2021 is poised to be a powerful sales event for restaurants.
Restaurants need new ways to navigate market trends
The restaurant industry faces consolidation, as 17% (110,000) of U.S. restaurants permanently closed in 2020, and 87% of full-service restaurants reported an average 36% drop in revenue. 3 These figures prove restaurant operators need help to boost their top line and cut costs as they adapt to intense rivalry and shifting market conditions.
During the pandemic, many consumers have embraced home for health or financial reasons or a creative outlet. Although 55% of consumers have been eating at home more often since the pandemic began, 65% say they are tired of cooking at home. 4
Fortunately, consumers are in a celebratory mood. Last year, Mother’s Day was a top sales day, as consumer spending at restaurants soared 103% on Mother’s Day Sunday and 63% on Saturday. 5 Restaurants can relieve consumers of the chore of cooking and add variety to dining occasions like Mother’s Day.
Successful restaurants gain a digital data advantage
To satisfy consumers’ needs and outplay rivals, restaurants now turn to data analytics from DataWeave to protect their profitability with effective pricing, menu and promotion decisions.
Pricing analytics
Restaurant operators can optimize their pricing to stay competitive. For instance, restaurants can compare their offerings and delivery fees with those of rivals to pinpoint and fill any gaps. Monitoring rivals’ pricing moves also helps restaurant operators stay flexible by keeping their prices affordable, so they can attract online sales growth.
Menu analytics
To minimize costs, more restaurants are streamlining their menus. Menu analytics can help operators spot the optimal mix of bestselling items and emerging food trends, like plant-based, vegan, gluten-free and local sourcing. To know which items to keep, operators can even use data insights on menu items down to the ZIP code level to localize their offerings and adapt to diverse tastes to drive online sales.
Promotion analytics
As consumers embrace home entertaining this Mother’s Day, restaurant operators can use data insights to boost sales.They can monitor rivals’ moves and compare their promotional strategies with those of competitors. Evaluating their digital marketing performance (like their brand’s discoverability and visibility ranking on food apps’ homepages) helps restaurants show up more prominently online and sell more.
Savvy restaurants welcome celebrations as lucrative sales occasions
Restaurants can spice up Mom’s life by letting her relax and receive the gifts of tasty meals, time savings and family festivities. Operators can simplify Mother’s Day celebrations by giving consumers a hassle-free dining experience so families can focus on connecting rather than cooking.
For a business advantage, restaurant operators can apply digital marketing insights to boost their agility in responding to consumers’ needs and rivals’ moves.
To stay agile and competitive as the food delivery market booms, leading restaurant chains and food delivery providers are collaborating with DataWeave to make data-driven pricing, menu and promotional decisions that fuel online sales.
1 Retail Holiday and Seasonal Trends: Mother’s Day. NRF. 2021 2 New Study Shows What Moms Really Want On Mother’s Day. US Foods. May 2020. 3 Valinsky, Jordan. 10,000 of America’s restaurants have closed in the past three months. CNN. December 9, 2020. 4 Contreras, Tricia. How the pandemic is shaping home cooking trends. SmartBrief. September 30, 2020. 5 Lalley, Heather. Despite pandemic, Mother’s Day was huge for restaurants. Restaurant Business. May 18, 2020.
Show moms extra love this year. With Mother’s Day coming up fast, savvy beauty and fashion brands will use this special occasion to inspire pampering and gift giving to fuel their e-commerce sales growth.
This year, beauty and fashion are poised to boom, as 40% of consumers plan to buy beauty products and 37% will buy new outfits for going out. 1 According to eMarketer,apparel and accessories e-commerce sales will grow nearly 19% this year due to pent-up demand for clothing, while health and beauty sales will rise 16%. 2
“People will be happy to go out again … there will be a fiesta in makeup and in fragrances.”
~L’Oréal CEO and Chairman Jean-Paul Agon
After beauty and apparel sales declined last year, brands now seize every opportunity to capitalize on the categories’ resurgence in 2021. To differentiate their goods, brands can use e-commerce marketing best practices to position their fashion and beauty items as spectacular gifts that moms will love.
Aligning with the latest trends can help brands boost online growth.
Hot trends dominating beauty and fashion
This Mother’s Day, shoppers can delight moms with beauty bestsellers like:
Mask-friendly makeup: As we continue to wear masks over the short-term, cosmetics like false lashes, smudge-proof mascara and ultra-hypoallergenic eyeshadow will remain popular. 3
Fragrances: Online fragrance sales rose 45% year-over-year in 2020. Clean and organic beauty categories grew 56% with fragrance brands growing the most. 4
Purpose-led brands: Consumers crave companies that care. More online searches contain keywords like “ethical beauty” and “sustainable makeup” for products that help consumers look good and feel good. 5
Online fashion is in vogue
Before the pandemic, consumers bought less than one-third of their apparel or footwear online; last year, the proportion surged to an astounding 51%. In 2021, consumers will invest even more in their wardrobes, including trends like:
Comfort: Athleisure will remain in demand as many consumers still prefer comfortable clothing when they work from home. 7
Beloved staples: Classic pieces like jeans, dresses and simple yet elegant tops are making a comeback as consumers start to go out more. 8
Retro ‘80s: Ladies are ready to party like it’s 1984. Bright and metallic colors and sequins for occasionwear (and even NFL linebacker-inspired shoulderpads) are recreating a fun, indulgent ’80s vibe. 9
Brands’ secret weapon for a competitive advantage
For successful Mother’s Day campaigns, more fashion and beauty brands will use digital shelf analytics for marketing decisions that maximize their ROI and e-commerce sales.
To ensure online shoppers discover Mother’s Day products with ease, brands are usingShare of Searchinsights to measure their share of digital shelf. These DataWeave analytics tell brands which keywords perform best. Brands can also benchmark their search and navigation visibility against rivals’ rankings across e-commerce categories, websites and geographic regions.
Using Content Auditinsights tells brands how their content is performing. They can discover and fill content gaps so their products show up more prominently. Optimizing content (like keywords, product page titles, descriptions, ads and sponsored space) and images to align with the retailers’ search algorithms ensures a consistent brand experience across all online channels. Improving content helps brands connect to consumers with marketing that resonates and inspires them to buy. Brands also use
Pricing and Promotionsinsights to measure the effectiveness of their online promotions and secure sales. Brands can stay competitive by ensuring their pricing and promotions are in line with rivals’ offers, such as identifying first movers and rivals with the deepest discounts across retailers and SKUs. Brands can even determine how imitating rivals’ pricing and promotional moves could impact revenue and sales volume.
Help shoppers make Mom’s day
Since Mother’s Day is almost here, beauty and fashion brands can apply these data insights to connect consumers with a variety of products moms will love. Digital shelf analytics from DataWeave can help brands deliver timely campaigns, improve their return on digital marketing spend and make effective marketing decisions to drive e-commerce sales.
1 Howland, Daphne. Wells Fargo sees permanent behavior shifts from the pandemic. Retail Dive. March 29, 2021. 2 Droesch, Blake. US Ecommerce by Category 2021. eMarketer. April 27, 2021. 3 Wood, Dana. Is Makeup Dead? The Robin Report. April 18, 2021. 4 Larson, Kristin. Fragrance Sales Pick Up As Consumers Reengage With The Outside World. Forbes. April 27, 2021. 5 What Can Brands Learn About Sustainability From Green Beauty Consumers? Beauty Business Journal. June 15, 2020. 6 Howland, Daphne. Wells Fargo sees permanent behavior shifts from the pandemic. Retail Dive. March 29, 2021. 7 Ibid. 8 Bhattarai, Abha. Americans are starting to buy real clothes again. The Washington Post. March 18, 2021. 9 Warren, Liz. Loose Denim and Bold Occasionwear on Full Display for Fall 2021. Sourcing Journal. April 2, 2021.
Soaring e-commerce growth has made retail more crowded, complex and competitive. Now retailers face an urgent need to keep an eye on more rivals with potential substitute products to maximize their own e-commerce growth.
Consider these recent figures, which illustrate online shoppers’ abundance of product choices:
24% year-over-year increase in direct-to-consumer (DTC) brands in the U.S. alone was estimated for 2020 as more brands bypass retailers1
55% of shoppers have purchased private label in the past year and many retailers are investing more in their own brands2
110% average increase in small retailers’ 2020 online holiday sales, as more players launched new e-commerce shops during the pandemic3
39% of U.S. consumers have changed brands, with the level of brand switching doubling in 2020 compared to 2019, especially among Gen Z and Millennial consumers, as loyalty declines4
These statistics prove that in 2021 retailers need to navigate more online players and products. Now retailers need a new approach to stay on top of market trends to keep their e-commerce strategies competitive, profitable and attractive to discerning online shoppers.
Retailers reduce the risk of substitutes with similarity matching
In response to online crowding, more leading retailers are turning to similarity matching. Similarity matching is a type of retail analytics that scour global e-commerce sites to find products that exactly match a specific item as well as products that closely match it. Similarity matching insights have grown in strategic significance because they increase retailers’ visibility into potential substitute products, so they can respond to all rivals’ moves with greater agility and efficiency to stay competitive.
In terms of e-commerce applications, similarity matching helps retailers gather insights on potential substitute products so they can adjust their pricing and assortment strategies accordingly. Retailers can align their pricing with rivals’ pricing moves for similar items to protect their margins and maximize profitability. They can also make informed assortment decisions, including which product mix of bestsellers, unique items and private labels could optimize their online sales performance.
Online shoppers search for productsdifferently across different categories
Consumer behavior plays a role, as online search habits differ across product categories, which influences the type of similarity matching retailers need. For example, categories like fashion, toys, home and kitchen work best with similarity matching based on text and images. In these highly-visual categories, consumers can quickly determine whether a product fits the design and aesthetic they are looking for. As a result, e-commerce product titles, descriptions and product images play a big role in consumers’ purchase decisions.
By contrast, consumer electronics and furniture are categories in which consumers tend to seek specific product attributes, such as a certain level of resolution for their high-definition TV or a couch with particular dimensions so it fits their living room. For these types of products, consumer purchases are driven by product specifications, so similarity matching takes into account their specific needs as well as a degree of tolerance for exact or near-similar attributes across online competitors.
Expect intense e-commerce rivalry in 2021
As more consumers shop online, they are increasingly informed by online product comparison information. A wide variety of product choices means consumers can substitute similar goods with ease, especially if a particular item is out-of-stock. Perceived product differentiation, price sensitivity and private labels can also influence consumers’ purchase decisions.
Across categories, e-commerce growth is outpacing total retail growth. When competition is this fierce, there is an increased risk that numerous and aggressive players will drive down profit margins. Leading retailers are now seizing opportunities to earn consumer loyalty. Using similarity matching helps retailers by offering in-demand products that consumers will actually buy and deliver exceptional online experiences to prevent shoppers from switching to rivals and their comparable products.
Similarity matching lets you stay competitive
As e-commerce traffic and rivalry increase, similarity matching helps retailers stand out and serve online shoppers more effectively.
Retailers gain visibility into their entire competitive landscape to keep their e-commerce strategy responsive to shifts among consumers and rivals. By knowing the full scope of potential substitute products available online, retailers can keep their pricing and assortment strategies in line with rivals’ to reduce their risk of losing sales to rivals, and boost their top line, profitability and cost savings.
The data insights give retailers the flexibility they need to align with online shoppers’ different needs across categories. As a result, retailers can use similarity matching to boost agility and gain a competitive advantage by adapting to online shoppers’ needs, winning their sales and fueling e-commerce growth.DataWeave’s similarity matching capability lets clients
1 US Direct-to-Consumer Ecommerce Sales Will Rise to Nearly $18 Billion in 2020. eMarketer. April 2, 2020.
2 Ochwat, Dan. Shopper study: Private brands purchased because they’re preferred. Store Brands. February 24, 2021 3 Miranda, Leticia. Small businesses who pivoted to e-commerce saw record sales during Black Friday weekend. December 1, 2020. 4 Charm, Tamara, Harrison Gillis, Anne Grimmelt, Grace Hua, Kelsey Robinson and Ramiro Sanchez Caballero. Survey: US consumer sentiment during the coronavirus crisis. McKinsey & Company. March 24, 2021.
The proliferation of informed shoppers, e-commerce sites, and competitors of all sizes has increased the complexity of – and lucrative opportunities in – brand management.
Now more brands rely on data insights to uncover specific ways to make their digital marketing more arresting, effective and profitable. Many brands struggle with e-commerce profitability due, in part, to advertising expenses that often yield lackluster results.1
Analytics are growing in retail significance, as 88% of retail and consumer goods marketers say data improves their marketing by allowing them to personalize touchpoints. Relevant marketing and great marketers helps brands connect with consumers. Let’s see why leading brands are adding data insights to their 2021 marketing strategies to fuel online sales growth.
Brands discover how to get discovered
Consumer goods brands no longer leave it up to chance that consumers will find them online. The digital migration of companies and consumers over the past year means more noise for brands to breakthrough.
Now search is growing in importance to improve brands’ online product discovery. Here’s why:
87% of shoppers begin their hunt in digital channels3
17% rise in paid search in late 20204
24% rise in paid social advertising during the same period5
To grab consumers’ attention by being easier to see, more brands are turning to data insights to track their online visibility.
Brands need to look for ways to mitigate the high costs of acquiring customers online6
Brands use marketing analytics related to keywords and navigation searches help brands know exactly how much space on the digital shelf they occupy across different online platforms.
These DataWeave’s Share of Search solutions help brands understand what percentage of the digital shelf they command through either keywords or navigation. These insights can help brands decide whether to boost their brand visibility using sponsored ads to ensure their products show up more prominently in online search results to boost brand reach and awareness on each channel. For instance, brands can tell whether consumers search for products using branded, generic or category-specific keywords to align their marketing accordingly.
In addition, brands can see how their organic and sponsored results rank compared to their competitors to spot ways to improve their visibility rank and decrease customer acquisition costs.
Content differentiates a brand’s digital shelf
For a striking digital presence and enhanced discoverability, leading brands measure how effectively their content inspires online shoppers to choose them.
Brands can improve their digital marketing results by using Content Audit insights to spot patterns among their top-performing campaigns. They can also benchmark their content with category bestsellers to discover how to optimize their online performance to grow sales volume and market share.
Strategic advertising requires high-quality photography and data-driven content7
Using these data insights from DataWeave helps brands determine how well their content (including product description pages and images) align with e-commerce algorithms and lead to online traffic, engagement and sales. Brands also adapt faster by adjusting underperforming campaigns to reduce costs and optimize their digital marketing spends.
Brands can fill content gaps across online channels with enhanced product information that aligns content and images with brands’ product information management (PIM). Using analytics to deliver a consistent brand experience across all online channels can help brands build relationships with consumers and earn their trust.
Alluring promotions help brands secure the sale
As e-commerce evolves, brands have matured beyond Google AdWords and Facebook campaigns to offer targeted promotions across digital touchpoints, which increases marketing reach and complexity.
To boost clarity, be in demand and drive sales across online platforms, more leading brands use data insights to measure the effectiveness of their digital Promotions. Promotional insights from DataWeave keep brands informed of trending categories and products to keep their online offerings relevant and timely. Brands can pinpoint exactly which products to promote and which e-commerce sites help them drive the most profitable results with compelling digital offers.
Brands that respond quickly to their customers’ needs have the upper hand8
Analytics also keep brands competitive and relevant by benchmarking their promotional strategies with their rivals’ and continuously monitoring rivals’ online moves. For instance, brands can track the promotions their competitors offer for similar products across different e-commerce sites. These competitive insights help brands quickly spot opportunities to optimize their online conversions with appealing promotions that reflect market trends.
Better marketing decisions can help brands grow sales and share
Data insights make brands more enticing by connecting the dots among their online visibility, content and promotions. Brands uncover ways to make smarter marketing decisions faster to improve their top line and decrease customer acquisition costs. DataWeave analytics also help brands stand out and improve product discovery, engagement and sales. As a result, brands save time and boost their agility with relevant marketing that resonates and inspires shoppers to keep coming back.
1 Jansen, Caroline, Cara Salpini and Maria Monteros. 8 DTC trends to watch in 2021. Retail Dive. February 3, 2021 2 Casna, Kathryn. Ecommerce Trends That Are Shaping the Way Businesses Sell Online. Salesforce. 2021. 3 Casna, Kathryn. Ecommerce Trends That Are Shaping the Way Businesses Sell Online. Salesforce. 2021. 4 The Future of eCommerce in 2021. Shopify Plus. 2021. 5 The Future of eCommerce in 2021. Shopify Plus. 2021. 6 Jansen, Caroline, Cara Salpini and Maria Monteros. 8 DTC trends to watch in 2021. Retail Dive. February 3, 2021. 7 Glasheen, Jasmine. 2021 Forecast: Next Gens in a Brand-New World. The Robin Report. January 3, 2021. 8 Monteros, Maria. Forrester: Few brands can anticipate and act on consumer needs. Retail Dive. February 10, 2021.
Brands that protect their bottom line will win online.
As global e-commerce smashes sales records, more brands are now taking control over their online presence (“digital shelf”) to enhance their performance and profit margins.
In the U.S., the increase in e-commerce penetration during the first half of 2020 was equivalent to that of the last decade.1 Last year also marked the first time in history that all retail sales gains came from e-commerce.2 E-commerce has lasting appeal, as two-thirds of consumers plan to continue to shop online after the pandemic.3
“Brands need to continue to look for ways to mitigate the high costs of acquiring customers online.” 4
To keep up as shopping migrates online, brands face bigger expenditures. In the second quarter of 2020, e-commerce costs grew much faster (up 60% year-over-year) than revenues (up 40%).5 Namely, brands face steep costs for customer acquisition and logistics, which erode their online profit margins.
The bottom line for brands is they must sell online – profitably – to stay competitive. They urgently need new ways to drive online sales and incur fewer costs. Let’s see why brand leaders are using data insights to optimize their e-commerce decisions and profitability.
Brands find new growth opportunities
Over the past year, e-commerce has gotten more crowded. Now brands seek proven ways to differentiate their offerings and consistently deliver an alluring online experience. That’s because a recent study found 42% of consumers cite less trust in online shopping due to poor experiences, such as inconsistent pricing and out-of-stock merchandise.6 In response, these e-commerce best practices can help brands improve the customer journey and top line sales.
To help consumers find their products online with ease, brands can use data insights for superior product discovery. Insights help brands know exactly which keywords shoppers search for to earn high visibility rankings among consumers’ online search results. Data analytics direct brands to the most relevant keywords, which they can use in marketing, including product descriptions, for effective online discovery.7
Brands also face increased pressure to keep up with rivals’ real-time pricing changes across retailers’ e-commerce sites, online marketplaces and social media. Insights help brands price competitively across channels by monitoring and promptly adapting to competitors’ online pricing moves. Brands can even use data to ensure merchants consistently respect pricing policies.
Data analytics also help brands measure their marketing effectiveness and popularity across e-commerce
websites, and how they compare to their rivals. Brands can improve how they promote their products by using targeted digital content that resonates. For instance, they can publish unique content on each channel tailored to the platform’s unique algorithm and use data to discover patterns among their top performing campaigns. Also, brands can determine when to use their own social media channels or pay for sponsored ads to drive more sales.
As we saw last March, in-stock merchandise is essential to maximize online sales. Data analytics help brands track their stock status to ensure products are available across all their digital channels for reliable service that sparks more sales.
Brands find new efficiencies
Cost effectiveness is also vital and these e-commerce best practices help brands boost their online efficiencies.
Brands use insights to pinpoint and keep sharing content that effectively resonates with and enages their target audience. They can use data insights to see where to allocate their marketing spend for online promotions and either revitalize or drop underperforming online promotions. Brands can also track whether their online promotions align with rivals’ promotions to stay competitive and agile.
Likewise, measuring a brand’s popularity through consumer reviews reveals which underperforming products to downplay to conserve marketing resources for the specific products and bundles that perform best in their categories. For instance, PepsiCo’s and Kraft Heinz’s new online shops offer only large items or bundles for basket sizes large enough to offset shipment costs.8
To reduce the high cost of product returns, brands can use data insights to prioritize bestselling products rather than items consumers are more likely to send back. Using clear, up-to-date content, including product descriptions with accurate dimensions, can also help online consumers know exactly what they’re buying to minimize returns.
How brands and consumers profit
When brands use insights to make better e-commerce decisions, they can compensate for ballooning expenses. Analytics help brands connect the dots among their online visibility, promotions, performance and reviews. These best practices can give brands an edge by uncovering how to be more aggressive with revenue-earning and cost-cutting opportunities. Brands find effective ways to acquire more online customers to improve their top line and offset e-commerce expenses with new efficiencies. Data-driven digital marketing decisions help brands improve their e-commerce effectiveness to stay profitable and competitive.
Meanwhile, consumers also win by having an inviting, smooth and reliable online shopping experience. They find the products they want with greater ease, and feel confident enough to buy based on information like a brand’s pricing, promotions and product availability.
1 Arora, Arun, Hamza Khan. Sajal Kohli and Caroline Tufft. DTC e-commerce: How consumer brands can get it right. McKinsey & Company. November 30, 2020. 2 Ali, Fareeha. US ecommerce grows 44.0% in 2020. Digital Commerce 360. January 29, 2021. 3 Arora, Arun, Hamza Khan. Sajal Kohli and Caroline Tufft. DTC e-commerce: How consumer brands can get it right. McKinsey & Company. November 30, 2020. 4 Jansen, Caroline, Cara Salpini and Maria Monteros. 8 DTC trends to watch in 2021. Retail Dive. February 3, 2021. 5 Haber, John. Logistics Costs Challenge E-Commerce Profit Margins. Parcel Industry. October 9, 2020. 6 O’Carroll, Derek. 5 Hidden Trends That Will Shape E-Commerce in 2021. Total Retail. February 4, 2020. 7 Leong, Brandon. COVID-19 strategy: Use the power of your digital sell sheet. Digital Commerce 360. August 23, 2020. 8 Arora, Arun, Hamza Khan. Sajal Kohli and Caroline Tufft. DTC e-commerce: How consumer brands can get it right. McKinsey & Company. November 30, 2020.
Black Friday looked downright sultry with desirable discounts on health and beauty products.
This year, health and beauty sales faced the threat of declining demand, as the pandemic keeps many consumers cooped up at home and in-store product testers no longer allowed. Yet consumers’ enduring desire to look and feel their best means this category will remain resilient. (Plus, we want to look smokin’ hot on Zoom.)
That’s why we were curious to know how retail rivals, ranging from discounters to department stores, are battling it out to become bodacious beauty destinations to win the hearts, wallets and fake lashes of online shoppers.
To calculate which retailers’ prices offered the broadest and most generous discounts, we examined health and beauty products’ pricing at Amazon, JC Penney, Macy’s, Neiman Marcus, Overstock, Nordstrom, Target and Walmart. We compared the pre-sale period (November 24-26) to the holiday sales period (Black Friday on November 27 through Cyber Monday on November 30) for a glimpse of retailers’ pricing strategies in this fiercely competitive category.
To gain insights into retailers’ competitive pricing strategies, we tracked three scenarios: whether prices decreased, increased or remained the same during the last week of November 2020. The vast majority of health and beauty products (91.0%) maintained the same prices during the pre-sale and sales periods. An astounding 99.7% of Target’s health and beauty prices stayed the same during the period.
Amazon had the highest proportion of health and beauty products that offered a price decrease (18.1%), particularly on men’s fragrance, women’s fragrance and men’s hair care. Offering discounts on more items hints that Amazon wants to attract more health and beauty consumers, including men, by making more items affordable. Target offered the lowest proportion of health and beauty products with price decreases (0.8%).
Amazon also had the greatest proportion of health and beauty products with a price increase (7.0%) with 15.3% of men’s fragrance earning a price hike.
On Black Friday, among health and beauty products with price decreases, Target gave the most generous average discount (37.6% vs. 7.2% for Overstock). However, Target’s discounts applied to only 12 products compared to 798 for Overstock.
Common types of health and beauty products with the highest average discount on Black Friday have included face makeup, men’s fragrance, men’s hair care, and shampoo and conditioner.
Among health and beauty products with price increases on Black Friday, Nordstrom had the highest average price hike (43.8% on one women’s fragrance) and Walmart offered the lowest (10.3% on 250 products).
These findings suggest that Target was willing to create aggressive loss leaders in this category and Amazon wanted to boost health and beauty sales among male shoppers.
Black Friday vs. Cyber Monday
This year, most retailers offered more additional discounts on health and beauty products on Cyber Monday than on Black Friday, possibly to prioritize clearing out their inventory before year-end. JC Penney and Macy’s were the exception. Overall, the top product types that received additional discounts included shave and hair removal, women’s fragrance and face makeup.
On Cyber Monday, Amazon offered additional discounts on the greatest proportion of health and beauty products (21.1% vs. 2.1% for Target). Amazon focused on men’s hair care, shampoo and conditioner and face makeup.
Half the retailers (Amazon, JC Penney, Nordstrom and Overstock) offered deeper additional discounts on health & beauty on Cyber Monday than Black Friday, possibly to clear out their inventory before the end of the year. Cyber Monday discounts ranged from 35.0% for Nordstrom to 7.8% for Overstock.
Meanwhile, both Neiman Marcus and Walmart offered the same levels of discounts on both Black Friday and Cyber Monday.
Overall, the types of health and beauty products with the deepest discounts on both Black Friday and Cyber Monday were shampoo and conditioner, men’s hair care and face makeup.
Additional discounts across products by “premiumness” level
For almost all the retailers, the percentage of health and beauty products with additional discounts was higher on Cyber Monday than on Black Friday. Overstock had the highest proportion (20.2%), slightly more than Amazon (20.0%).
JC Penney had a higher percentage of products with additional discounts on Black Friday. Neiman Marcus had the same percentage of products on both sales days.
All the retailers except JC Penney and Neiman Marcus allocated the greatest percentage of their additional discounts to health and beauty products at the high level of premium. The retailers may have wanted to appeal to upscale shoppers and make high premium goods more accessible to a broader audience of consumers.
Half of the retailers (JC Penney, Nordstrom, Target and Walmart) offered deeper discounts on Black Friday than Cyber Monday.
Target offered the most generous discounts on Black Friday with an average additional discount of 39.7%, which ranged from 50.1% on moderately premium health and beauty products to 28.6% for products at a high premium level. Target appeared to make more beauty items, including high premium items, affordable to more consumers to stay competitive as a beauty destination.
Conversely, Amazon, Overstock and Macy’s were more generous with additional discounts on Cyber Monday. Among the high premium level of health and beauty products on Cyber Monday, Macy’s offered the deepest discounts (31.3%), edging out department store rival JC Penney (30.4%) in competing for upscale shoppers.
Health & Beauty’s Ravishing Holiday Prices
This year’s Black Friday and Cyber Monday pricing strategies showed retailers’ attempts to stand out, expand their market reach to stay competitive. Appealing to a broader audience included spanning upscale and value tiers, and wooing more male online shoppers to grow their top line and boost loyalty in an intense category amid a pandemic.
Stay tuned for more Black Friday and Cyber Monday 2020 analysis to discover how retailers strategically price their products to win in leading e-commerce categories.
This year’s COVID-19 pandemic created a seismic shift towards tech, directly affecting retailers’ Black Friday and Cyber Monday pricing strategies for electronics. Prime Day 2020’s new fall date also inevitably influenced pricing and purchasing patterns. If consumers pampered themselves with a 75-inch TV in October, what are the odds they’re in the market for another big-screen TV in late November?
Consumer electronics are perennial holiday bestsellers because they make gift-giving easy, whether we buy them for others or for personal indulgence. Continuous innovation also means a comparatively shorter product lifecycle, making electronics an exciting, progressive retail category.
To determine which retailers’ pricing strategies offered the most generous discounts on electronic products, we examined electronics pricing at Amazon, Best Buy, Overstock, Target and Walmart. We compared during the pre-sale period (November 24-26) to the holiday sales period (Black Friday on November 27 through Cyber Monday on November 30) for a glimpse of retailers’ pricing strategies to stay competitive in 2020.
For competitive pricing insights, we tracked three scenarios before and during 2020’s traditional holiday sales season: whether prices decreased, increased or remained the same. Most strikingly, the overwhelming majority of electronics products (89.8%) maintained the same prices during the pre-sale and sales periods. For instance, Target kept a whopping 98.0% of its electronics prices the same during the period.
Amazon had the greatest proportion of electronics products that offered a price decrease (11.7%), particularly on laptops, mobiles and wearable technology. These results also suggest Amazon wants to reach more consumers by making more electronics affordable with discounts. Target offered the lowest proportion of electronics with price decreases (2.5%).
Overstock had the greatest proportion of electronics products that offered a price increase (10.7%) with 30.3% of TVs increasing in price. Best Buy offered the lowest proportion of electronics with price increases (1.2%).
Among electronics products with price decreases on Black Friday, Best Buy offered the highest average discount (16.6%) and Amazon offered the lowest (10.2%). Among all the retailers, the types of electronics with the highest average discount included tablets, headphones, laptops and TVs.
Among electronics products with price increases on Black Friday, Best Buy had the highest average price hike (30.2%) and Amazon offered the lowest (9.8%). That said, Best Buy increased the price of one laptop by 73.1% whereas Amazon increased the price of 44 laptops by an average of 4.2%.
These findings show that Best Buy aggressively protected its market share in this competitive category by offering the most generous discounts.
Black Friday vs. Cyber Monday
Without exception, the retailers offered more additional discounts across the electronics category on Cyber Monday than on Black Friday. Retailers may have wanted to clear out their inventory to make room for new, innovative products in their assortments.
Amazon had the greatest proportion of electronics with additional discounts on Cyber Monday (15.7%, which is more than double the 7.3% each for Overstock and Target). Amazon’s additional discounts focused on mobiles, laptops and wearable technology.
Overall, the greatest proportion of additional discounts on electronics on Cyber Monday focused on laptops, desktops and USB flash drives.
While most retailers offered deeper discounts on electronics on Cyber Monday than Black Friday, Overstock was the sole exception.
On Cyber Monday, Target offered the most generous average additional discounts (19.6% vs. 10.2% for Amazon); however, Target’s discounts applied to 260 electronics products compared to 924 for Amazon.
Overall, the types of electronics with the deepest discounts on Cyber Monday on electronics were USB flash drives, tablets and headphones.
Additional discounts across products by “premiumness” level
When we examine electronics’ additional discounts according to the products’ premium level, several patterns stand out.
Most apparent is that every retailer offered a higher proportion of additional discounts on Cyber Monday compared to Black Friday, ranging from 15.9% for Amazon to 6.4% for Best Buy.
With only one exception, Amazon offered the greatest proportion of additional discounts across all premium levels. Only Target offered a slightly higher proportion among low premium electronics (11.9% vs. 11.3% for Amazon). This approach could help Amazon make more electronics products more affordable to more consumers and boost its reach in this competitive category.
Among electronic items at the high premium level, Amazon was most aggressive in allocating additional discounts (21.0% vs. 5.4% for Target), which could help the e-commerce giant earn top-of-mind status among affluent shoppers in the market for big-ticket electronics.
Most retailers (Amazon, Best Buy and Walmart) offered deeper discounts on Cyber Monday than Black Friday. By contrast, Overstock and Target were more generous on Black Friday.
Interestingly, Target’s average additional discount on Cyber Monday (18.8%) was still more generous than those of the other retailers.
Among moderately premium items, Target’s average additional discount was 22.3%, more than double Amazon’s 10.3%. Target may have tried to make mid-market electronics more affordable to its core audience of value-seeking shoppers.
Additional discounts across products by “popularity” level
A review of retailers’ additional discounts by electronics’ popularity level reveals that most retailers allocated a bigger proportion of discounts on Cyber Monday than on Black Friday. Overstock was the exception. Again, clearing out 2020 inventory before year-end likely influenced retailers’ pricing strategies.
Overall, on Cyber Monday retailers showed a direct relationship between additional discounts and electronics’ popularity levels. For instance, Amazon offered additional discounts on 21.7% of highly popular electronics and 15.3% on moderately popular electronics. Since Amazon strives to be “The Everything Store,” it makes sense to make more products more appealing and affordable to more consumers. Meanwhile, Target offered nearly double the proportion of additional discounts of less popular electronics than discount rival Walmart (12.5% vs. 6.7%) to tempt value-seekers with deals.
Most retailers (Amazon, Best Buy, Target and Walmart) offered deeper discounts on Cyber Monday than Black Friday. Overstock was more generous on Black Friday.
On Cyber Monday, Target’s average additional discount (21.8%) was the most generous of all the retailers, nearly double that of Amazon (11.1%). However, Target’s discounts applied to 259 electronics products. vs. 903 for Amazon.
Both Amazon and Overstock gave their most generous discounts to less-popular electronics, possibly to clear out their inventory to make room for more popular or higher-margin items.
Black Friday & Cyber Monday 2020 Electronics Pricing Strategies
This year, the pandemic jolted consumers to focus on digital technology to stay connected to work, school and retail, which heightened demand for electronics.
In response, retailers’ 2020 pricing strategies for Black Friday and Cyber Monday suggest a desire to extend their reach beyond their core audience to maximize their brand appeal and steal rivals’ market share.
The Cyber Monday findings, in particular, suggest retailers decluttered their assortments to make space for the latest and highest-margin tech gadgets in time for Christmas.
Click here for more Black Friday and Cyber Monday 2020 analysis for greater clarity on the evolving pricing positions of retail rivals across top e-commerce categories.
As most of us stay home for the holidays this year, retailers hope we’ll invest in our nest.
The global pandemic ignited sales in the red-hot home furniture category, as our domestic comfort, functionality and aesthetics suddenly became urgent priorities in 2020. Now we’re investing more in domestic leisure, organizing and redecorating as our consumption habits shift.
That’s why we wanted to know how retailers adapted their Black Friday and Cyber Monday furniture pricing strategies to meet consumers’ needs. For instance, did retailers give the best deals on low-commitment furniture like area rugs or on big-ticket items like dining room sets?
To pinpoint which retailers offered the greatest proportion of discounts and the deepest discounts, we reviewed furniture at Amazon, Home Depot, JC Penney, Overstock, Target, Walmart and Wayfair. We compared the pre-sale period (November 24-26) to the holiday sales period (Black Friday on November 27 through Cyber Monday on November 30) to monitor retailers’ furniture pricing moves.
Top product types by additional discount
To review retailers’ holiday pricing strategies in the furniture category, we tracked three scenarios: whether prices decreased, increased or remained the same during the last week of November 2020.
The proportion of furniture items that maintained the same prices during the pre-sale and sales periods ranged widely, from 91.6% for Home Depot to only 22.6% for Wayfair.
That’s because Wayfair had by far the highest proportion of furniture with a price decrease (77.2% on 4254 products vs. 7.1% on 342 products for Home Depot). Since Wayfair specializes in home décor, it makes sense for the retailer to aggressively distribute discounts across its furniture assortment.
Among all retailers, the top types of furniture with discounts included bookcases, entertainment units, sofas and storage and cabinets. These findings suggest we are investing in domestic leisure, relaxation and organization.
On Black Friday, JC Penney offered the most generous average discount (21.3% vs. 4.6% for Wayfair). This means that although Wayfair spread discounts across its furniture subcategories, the actual discounts were lower than rivals’.
Types of furniture with the highest average discount on Black Friday at JC Penney included storage and cabinets, bookcases and rugs. These products tend to be affordable additions to our homes compared to bigger investments like a dining room set.
Black Friday Vs. Cyber Monday
On Cyber Monday, Target offered additional discounts on the greatest proportion of furniture (27.4% vs. 7.8% for JC Penney). These findings suggest Target really wants to be a convenient option for shoppers, including their home furnishing needs.
Among all retailers, the top types of discounted furniture included rugs, beds and entertainment units.
Most retailers offered deeper additional discounts on furniture on Cyber Monday than Black Friday, possibly to maximize sales before the end of the year. Cyber Monday discounts for furniture ranged from 20.9% for JC Penney to 4.9% for Wayfair. Among all retailers, the top types of furniture that received discounts on Cyber Monday were rugs, storage and cabinets and dining table sets, which show that redecorating and organizing were in style this holiday season.
Additional discounts across product “premiumness” levels
Nearly all retailers had a higher proportion of furniture with additional discounts on Cyber Monday than on Black Friday. Wayfair had the highest proportion (79.2% for both Cyber Monday and Black Friday vs. 9.3 for Home Depot). Wayfair’s use of discounts across all premium levels on both major sales days shows the retailer wants to extend its brand reach, own this category and earn top-of-mind status across diverse furniture shoppers at all price points.
Only JC Penney had a higher percentage of furniture with additional discounts on Black Friday.
Every retailer offered deeper discounts on Cyber Monday than Black Friday, likely to clear out 2020 merchandise.
On Cyber Monday, JC Penney offered the most generous furniture discounts, with an average additional discount of 38.0%, which ranged from 35.1% at the low premium level to 40.9% at the moderately premium level. Aggressive discounts could set JC Penney apart among department stores and attract more low- to mid-market consumers with tempting furniture deals.
Also, most retailers gave the deepest discounts on furniture at the high premium level, which can help to make upscale items accessible and affordable to a greater number of consumers.
Additional discounts across product “popularity” levels
Almost all retailers offered a greater proportion of additional furniture discounts on Cyber Monday than on Black Friday, ranging from 67.6% for Wayfair to 7.8% for Home Depot.
Across all levels of popularity for furniture, Wayfair dominated with discounts on the most diverse array of products to give more furniture shoppers an opportunity to save money.
Meanwhile, Amazon and JC Penney offered the greatest proportion of discounts at the low level of popularity, possibly to declutter their furniture assortment to make room for in-demand products.
Nearly every retailer offered deeper discounts on furniture on Cyber Monday than on Black Friday, with JC Penney being the most generous (39.1% vs. 5.3% for Wayfair).
Home Depot and Wayfair prioritized discounts among less popular furniture, likely to clear them out and make more room in their assortments for items people really want.
Amazon and Walmart gave deeper discounts on highly popular furniture to battle it out over bestsellers.
Holiday Furniture Pricing Inspires Us to Reimagine Our Space
This year, more consumers decluttered their homes and more retailers decluttered their furniture assortment by clearing out 2020 merchandise with desirable deals on Black Friday and Cyber Monday.
The findings of this pricing analysis hint at retailers’ competitive positioning. To own the furniture category, Wayfair aggressively allocated discounts across its assortment even if rivals gave deeper discounts. Target’s deep discounts helped to position the chain as a convenient option for shoppers’ cross-category needs, including furniture. JC Penney’s astonishingly deep discounts on Black Friday and Cyber Monday could help to liquidate inventory, whereas Amazon and Walmart battled over bestsellers.
Click here for more Black Friday and Cyber Monday analysis to learn about retailers’ holiday pricing strategies during 2020’s e-commerce boom.
Retailers really wanted to dress us up this holiday season.
This year’s Black Friday and Cyber Monday fashion pricing trends reflect how retailers have responded to the pandemic’s influence on apparel shopping to boost their resilience and competitiveness.
For instance, since most consumers now cocoon at home, few of us are likely to splurge on fancy gowns or suits as holiday gifts for ourselves or others. That’s why we wanted to know which retailers doubled down on Black Friday fashion discounts and which ones used Cyber Monday discounts to make room for in-demand merchandise.
To calculate which retailers’ prices offered the greatest proportion of discounts and the deepest discounts, we analyzed men’s and women’s fashions at Amazon, Bloomingdale’s, JC Penney, Macy’s, Neiman Marcus, Overstock, Nordstrom, Target and Walmart. We compared the pre-sale period (November 24-26) to the holiday sales period (Black Friday on November 27 through Cyber Monday on November 30) to gain insights into retailers’ pricing strategies in fashion.
Top product types by additional discounts- Men’s fashion
To review retailers’ holiday pricing strategies, we tracked three scenarios: whether prices decreased, increased or remained the same during the last week of November 2020.
The overall proportion of men’s fashion items that maintained the same prices during the pre-sale and sales periods was 88.6%, ranging from 99.5% for JC Penney to 75.0% for Neiman Marcus.
Neiman Marcus had the highest proportion of men’s fashions with a price decrease (25.0% vs. 1.4% for JC Penney). Top types of men’s fashions that had discounts were formal shoes, jackets and coats, and sports shoes. These findings seem to reflect how we rarely go out during the pandemic yet we’re exercising more.
In addition, Amazon and Walmart were most active in offering discounts across all men’s fashion subcategories with Amazon offering more than double Walmart’s percentage of products discounted (15.9% vs. 7.1%).
On Black Friday, JC Penney offered the most generous average discounts (35.6% vs. 9.4% for Overstock). While that contrast seems dramatic, it’s important to note JC Penney’s discounts applied to only 8 products compared to 929 for Overstock.
Men’s fashions with the highest average discount on Black Friday included formal shoes, jackets and coats and jeans.
Top product types by additional discounts- Women’s fashion
For women’s fashions we also tracked whether prices decreased, increased or remained the same during the last week of November 2020. The vast majority of women’s fashions (89.3%) maintained the same prices during the pre-sale and sales periods. A whopping 99.3% of Target’s women’s fashion prices stayed the same.
Neiman Marcus had the highest proportion of women’s fashions with a price decrease (33.4%), particularly on casual shoes, t-shirts and lingerie. JC Penney and Target offered the lowest proportion of price decreases on women’s fashions (1.9%).
Similar to men’s fashions, Amazon and Walmart offered price discounts across all the women’s fashion subcategories with Amazon offering a higher proportion of products with discounts. (10.7% vs. 7.7% for Walmart)
On Black Friday, JC Penney offered the most generous average discounts (45.0% vs. 12.2% for Overstock) yet JC Penney’s discounts applied to only 28 products compared to 1952 for Overstock.
The types of women’s fashions with the highest average discount on Black Friday included tops, casual shoes and handbags. Perhaps women pampered themselves with a new purse and new tops to look chic on Zoom calls.
Black Friday Vs Cyber Monday
During this year’s holiday sales events, almost all retailers offered more additional discounts on men’s and women’s fashion on Cyber Monday than on Black Friday, possibly to sell off seasonal inventory before year-end. Nordstrom was the only exception, offering more discounts on Black Friday.
On Cyber Monday, Target offered additional discounts on the greatest proportion of men’s fashions (63.3% vs. 10.6% for Walmart). Top types of men’s fashions with discounts included underwear, jeans, jackets and coats.
Similarly, Target offered additional discounts on the greatest proportion of women’s fashions on Cyber Monday (79.4% vs. 3.2% for JC Penney). The most common types of discounted women’s fashions were dresses and jumpsuits, t-shirts and casual shoes.
These findings suggest Target is aggressively pursuing value shoppers and positioning the chain as a convenient source for all the whole family’s apparel needs.
Most retailers (Amazon, Nordstrom, Overstock, Target and Walmart) offered deeper additional discounts on men’s fashions on Cyber Monday than Black Friday, possibly to maximize year-end sales and clear out seasonal inventory. Cyber Monday discounts for men’s fashions ranged from 29.8% for Nordstrom to 11.0% for Overstock. Top types of men’s fashions that received Cyber Monday discounts included jackets and coats, formal shoes, sunglasses and t-shirts, which reflect how men are going out less.
Conversely, most retailers (JC Penney, Macy’s, Neiman Marcus, Nordstrom and Walmart) offered deeper additional discounts on women’s fashions on Black Friday than Cyber Monday, possibly to entice women to get a jumpstart on the holiday sales weekend to maximize top line performance in this competitive category. Black Friday discounts for women’s fashions ranged from 45.0% for JC Penney to 12.2% for Overstock. Top types of women’s fashions with Black Friday discounts included swimwear, lingerie and t-shirts, which reflect seasonal merchandise.
Additional discounts across products by “premiumness” level
For almost every retailer, the percentage of fashions with additional discounts was higher on Cyber Monday than on Black Friday. Target had the highest proportion (62.7% vs. 5.7% for JC Penney). It appears Target really wants to win value-seeking apparel shoppers, by offering additional discounts on 93.3% of fashions at the low premium level (vs. 4.6% for Walmart).
By contrast, Nordstrom had a higher percentage of fashions with additional discounts on Black Friday.
Most retailers (Amazon, Bloomingdale’s, Neiman Marcus, Overstock, Target and Walmart) offered deeper discounts on Cyber Monday than Black Friday, likely make room for new seasonal merchandise.
Neiman Marcus offered the most generous fashion discounts on Cyber Monday with an average additional discount of 30.1%, which ranged from 31.7% at the high premium level to 28.9% at the low premium level. This aggressive discounting could help Neiman Marcus stand out among department stores, and extend its reach and appeal by making fashions more affordable across price points.
Conversely, JC Penney, Macy’s and Nordstrom offered deeper discounts on Black Friday. All three department stores were most generous at the low premium level for fashions, with JC Penney offering the deepest discounts (47.8%) to turn low premium fashions into irresistible Black Friday bargains.
Additional discounts across products by “popularity” level
Almost all retailers offered a greater proportion of additional fashion discounts on Cyber Monday than on Black Friday, ranging from 69.2% for Target to 5.2% for JC Penney, with a direct relationship between product popularity and additional discount percentage. Across all levels of popularity for fashions, Target was by far the most aggressive with discounts to appeal to the broadest variety of fashion shoppers.
Only Nordstrom offered a higher proportion of additional discounts on fashions on Black Friday, focusing on both high and low levels of popularity.
Most retailers (Amazon, Neiman Marcus, Overstock, Target and Walmart) offered deeper fashion discounts on Cyber Monday than on Black Friday, with both Neiman Marcus and Target being the most generous (28.8%). Amazon and Neiman Marcus were most generous with discounts among less popular items, while Overstock, Target and Walmart were most generous among moderately popular fashions.
Conversely, JC Penney, Macy’s and Nordstrom offered more generous fashion discounts on Black Friday, with JC Penney being the most generous (39.2%). All three retailers offered the deepest discounts at the low level of popularity, possibly to make room for in-demand fashion items.
2020’s Fashionable Holiday Prices
As this year’s Black Friday and Cyber Monday fashion pricing results show, we prioritized comfort and basics over debonair formalwear. Since staying at home is in style, many retailers discounted dressier attire.
In terms of competitive pricing strategies, Target’s aggressive discounts could boost the chain’s appeal among diverse fashion shoppers. Also, Neiman Marcus stood out among department stores by extending its reach and affordability across pricing tiers.
Click here for more Black Friday and Cyber Monday analysis to learn about retailers’ holiday pricing strategies during 2020’s e-commerce boom.
‘Make in India’ is a headline you’re likely to have seen smeared across newspapers or as campaign rhetoric. E-retailers in India were mandated to display the country of origin against all the products starting 1st August, 2020. The rationale behind this move was to provide the customer the information which would aid the government to accelerate their plans on curbing imports.
To get an idea of the extent that this was followed, we looked at 29,000+ products on Amazon and 20,000+ products on Flipkart, across popular categories.
What our data revealed (illustrated in the chart above) is that Flipkart had updated the country of origin information for 91% of the products while Amazon had updated for only 56%. In categories like Grocery/ Cooking Essentials and Personal Care, Flipkart updated this information across 100% of products that we looked at.
The chart above reveals interesting insights into the respective product mixes of Flipkart and Amazon. We narrowed our study to three categories that stood out; Electronics, Baby products and Men’s’ fashion. These are the categories we noticed that have the most number of products that are manufactured out of India. Apart from India, the largest manufacturer is China, where a lot of these products come from. Looking at this chart, we see that most of Flipkart’s products are manufactured in India, compared to its counterpart.
To sum up, we noticed that Flipkart has updated 91% of its products while Amazon has updated only 56% of their products of the products we tracked.
With the heightened emphasis on Make in India and reducing imports, sellers importing from other countries might have to rethink how to replace the products they currently are sourcing with local products. This also provides an opportunity to the Indian manufacturers to produce popular products which are currently being imported.
We’ll now have to wait and watch over the coming months to see how things unfold for these retailers and the sellers.
Amazon’s pricing advantage has declined in key categories, compared to last year as we enter 2020’s holiday season.
The holidays are here and the retail industry is gearing up for the yearly stampede. In a report published by Bain & Company, in partnership with DataWeave, it was observed that, “When it comes to pricing, Amazon’s historical advantage is also deteriorating. The research shows that in October and November 2019, Amazon matched or beat competitors’ prices 81% of the time in the categories studied. By November of 2020, that rate dropped to 74%”. This was based on the four key categories where we had pricing data for Amazon and at least one other competitor.
Amazon’s pricing advantage has declined in key categories
Aggressive pricing, which was once Amazon’s forte, seems to be on a downward trend this year. All but one category saw an increase in the percentage of products where they beat the lowest price, ‘movies, music, video games’ – by a small margin of one percentage point.
What could this shift be attributed to? The obvious would be the repercussions of COVID but there perhaps is more at work here. As observed last year, the behemoth that Amazon is, does not deter its competitors from constantly biting at the heels, with a steely determination to rope in market share. Everything from increased and specific customer demands, to government legislation, there are a lot of moving parts.
One thing is for sure, this is surely just the beginning of the great e-commerce battle. For access to the full article that was published in the Retail Holiday Newsletter by Bain & Company and powered by DataWeave, click here.
The rapid evolution in essential goods is rattling retail. That’s because the COVID-19 pandemic has dramatically changed shopping habits and retail necessities, leading to unpredictable shifts in demand.
Most notably, U.S. e-commerce has surged by an astonishing 45% year-over-year, as the pandemic accelerated online shopping by five years.[1] Since more consumers now work and learn from home, many pandemic-inspired habits will likely shape retail for years to come.[2]
Now that the risk of the second wave lies ahead, it’s the ideal time for retailers to review pandemic bestsellers and patterns to adapt to shifts in shopping behavior.
Pandemic’s bestsellers shape retail strategies
2020’s unexpected consumption patterns give retailers a glimpse of how they can adapt and thrive. The best-selling essential goods during the pandemic have included:
Toilet paper: +734% year-over-year (YoY) growth in March[3]
Disposable gloves: +670% in March[4]
Fitness equipment: + 535% YoY in online sales for February to March[5]
Hand sanitizer: +470% YoY for the week ending March 7[6]
Yeast: +410% YoY for the four weeks ending April 11[7]
Puzzles: +370% YoY in the last two weeks of March
Pyjamas: + 143% in online sales between March and April[8]
As such, retailers can ensure their assortments contain these types of popular cross-category items, which reflect overall themes of consumers’ needs for self-sufficiency, wellness and comfort.
E-grocery is also soaring, as experts predict a 40% rise in U.S. online grocery sales in 2020 due to the pandemic.[9] Top categories bought by online grocery shoppers include:
Packaged non-fresh food (69%)
Toiletries, personal care and diapers (63%)
Household cleaning and paper products (61%)[10]
In response to these trends, retailers can prioritize shelf-stable center store products and non-food consumer goods throughout the pandemic.
How retailers boost agility, clarity and sales amid COVID-19 chaos
Consumer panic led to pricing volatility for hard-to-find items like hand sanitizer, disinfectant wipes and masks.[11] To keep up with competitors’ online price fluctuations, more retailers use competitive analytics to adapt their own prices accordingly. Notably, McKinsey & Company cites data insights and price sensitivity as the top two disruptive trends the pandemic has turbocharged.[12]
In March, shortages of toilet paper and flour led consumers to react with panic and hoarding that created urgent supply chain issues. To avoid out-of-stock items, more retailers now turn to data insights to identify potential disruptions. Up-to-date insights help retailers spot emerging market trends and adapt their assortment to stock in-demand items.
Now that more consumers shop online, retailers are investing in digital promotions to boost sales. Data analytics help retailers quickly evaluate the effectiveness of their promotions, which can inspire consumers to fill their baskets. Nimbly adapting to competitors’ promotions is essential, as McKinsey cites rising competition for deals among the pandemic’s most disruptive retail trends.[13]
Avoid empty shelves: The pandemic has motivated more retailers to rely on data insights to make fast, effective pricing and assortment decisions.
As consumption habits evolve, high-level dashboards help retailers quickly spot inventory shortages to prevent out-of-stocks.
To make their retail strategies pandemic-proof, leading retailers are collaborating with DataWeave to access accurate, actionable insights that boost online agility and sales. Applying DataWeave’s trusted data gives retailers clarity amid today’s chaotic market and shifting demand for essential goods, so they can make effective decisions fast. Insights also help retailers enhance the customer experience by supporting in-stock product assortments, competitive pricing and effective promotions that boost sales, trust and loyalty. To see how DataWeave helps retailers stay agile and competitive, visit dataweave.com.
[1] Perez, Sarah. COVID-19 pandemic accelerated shift to e-commerce by 5 years, new report says. TechCrunch. August 24, 2020.
[2] Gottlieb, David. 5 Strategic Imperatives for Retail’s New Normal. Total Retail. August 18, 2020.
[3] Weiczner, Jen. The case of the missing toilet paper: How the coronavirus exposed U.S. supply chain flaws. Fortune. May 18, 2020.
[4] Clement, J. COVID-19 impact on fastest growing e-commerce categories in the U.S. 2020. Statista. June 19, 2020.
[5] Gibson, Kate. Coronavirus inspires fitness buying binge that tops New Year’s. CBS News. April 1, 2020.
[6] Chasark, Krisann. Coronavirus impact: Hair dye becoming next high-demand item amid COVID-19 pandemic. ABC News. April 11, 2020.
[7] Guynn, Jessica and Kelly Tyko. Dry yeast flew off shelves during coronavirus pantry stocking. Here’s when you can buy it again. USA Today. April 23, 2020
[8] Thomas, Lauren. Comfort is en vogue during coronavirus: PJ sales surge 143%, pants sales fall 13%. CNBC. May 12, 2020.
[9] Redman, Russell. Online grocery sales to grow 40% in 2020. Supermarket News. May 11, 2020.
[10] Redman, Russell. Online grocery sales to grow 40% in 2020. Supermarket News. May 11, 2020.
[11] Levenson, Michael. Price Gouging Complaints Surge Amid Coronavirus Pandemic. The New York Times. March 27, 2020.
[12] Kopka, Udo, Eldon Little, Jessica Moulton, René Schmutzler, and Patrick Simon. What got us here won’t get us there: A new model for the consumer goods industry. McKinsey & Company. July 30, 2020.
[13] Kopka, Udo, Eldon Little, Jessica Moulton, René Schmutzler, and Patrick Simon. What got us here won’t get us there: A new model for the consumer goods industry. McKinsey & Company. July 30, 2020.
Brands that build consumer trust will win big as online shopping explodes this year. As the COVID-19 pandemic propels more shoppers online, an astounding $5 trillion (30%) of annual global retail sales is up for grabs as the market shifts to e-commerce, according to Boston Consulting Group.[1]
Notably, e-commerce has changed brands’ retail processes. Unlike brick-and-mortar stores, the digital shelf is where brands manage their company and products among online shoppers, influencing what they browse and buy.
Rather than stocking merchandise through retailers’ physical stores, brands can now manage their online products by working with logistics experts like Fulfillment by Amazon. Instead of merchandising in stores with planograms and endcap displays, brands promote their digital assortment with targeted product content that resonates and keeps them coming back.[2] The evolution of retail can help brands save time and effort, and increase their agility and effectiveness.
Gaining high visibility on the digital shelf can help brands boost their reach, brand awareness and sales. That’s why more brands are now investing in proven e-commerce best practices to increase consumer confidence by offering reliable products, relevant marketing and a smooth online experience. Now that the 2020 holiday sales season is underway, it’s the perfect time to see how leading brands compete in the increasingly crowded online market by demonstrating credibility and consistency.
Market fragmentation increases brand complexity
Selling across multiple online touchpoints means brands have more e-commerce websites and digital shelves to monitor to ensure compliance with brand guidelines to ensuring a consistent customer experience. To engage online shoppers, brands’ marketing strategies must now diligently manage their digital shelf across diverse online shopping arenas, including:
Direct-to-consumer (DTC) sites: More brand manufacturers are shortening the supply chain by bypassing retailers and selling directly to consumers, including sales through their own e-commerce websites.eMarketer predicts that U.S. DTC e-commerce sales will grow by 24% to reach nearly $18 billion in 2020.[3]
Retailers’ e-commerce sites: Brands are also migrating online because the retailers who sell their merchandise moved online this yearto adapt to the pandemic and survive shuttered storefronts. As of April 21, e-commerce grew 129% year-over-yearin U.S. and Canadian orders and U.S. e-commerce sales are on track to hit nearly $710 billion this year.[4] More than ever, sharing timely, accurate product information with retail partners is essential for success.
Online marketplaces: A growing number of brands are investing in digital advertising and content to stand out on popular, high-traffic online marketplaces. Global e-commerce leaders Amazon, eBay and China’s Alibaba and JD, are mostly search-based sites, as users know what they want and search for it, which makes product description pages and ads important marketing tools. Conversely, China’s Pinduoduo platform involves group-buying and interactive games to boost brand awareness and sales by entertaining online consumers and inspiring flattering word-of-mouth.[5] Among online marketplaces, digital content fuels brand discovery and sales.
Last-mile delivery channels: Brands can also sell in collaboration with their last-mile partners. Last-mile experts like Peapod, Instacart, Uber and Shipt offer online advertising opportunities for brands to reach new audiences. For instance, Instacart launched a self-serve advertising platform that lets brands promote their products in search results. Brands can choose the products to promote, set a budget and pay when users engage with those products.[6]
Social media: To reach and influence consumers where they already spend their time, more brands are investing in social media promotions and even embracing social commerce innovation. Social media matters to brands’ marketing effectiveness, as 52% of online brand discovery happens on social feeds.[7] Also, 92% of Instagram users say they’ve followed a brand, visited their website or made a purchase after seeing a product on Instagram.[8]
Brand promotions have evolved beyond Google AdWords and Facebook campaigns. Now promotions include digital content and ads across all of these digital touchpoints, which increases the scope of brand marketing efforts to reach online consumers.
How brands transform digital shelf complexity into clarity
To earn online shoppers’ trust across e-commerce arenas, more leading companies are turning to a common solution: data.
Too often, online shoppers abandon their cart due to concerns that they will unwittingly buy inauthentic products from fraudulent sellers. To protect their brands, manufacturers use data insights to pinpoint and prevent unauthorized sellers, counterfeit products and minimum advertised price (MAP) violations to demonstrate authenticity, accountability and price parity.
“Digital is the new normal” ~ Nike CEO John Donahoe[9]
To invigorate underperforming online promotions, brands rely on analytics to connect the dots among their online promotions, marketing performance and share of voice. Insights on advertising, keywords and consumer reviews help brands make better marketing decisions faster. These insights help brands stand out from competitors and build relationships with shoppers by ensuring their promotions resonate and drive more sales online.
To overcome low online traffic and sales, more brands apply data insights to improve their digital presence, visibility and sell-through rates. Brand analytics measure their popularity on e-commerce websites and track their stock status to improve accessibility, optimize digital shelf velocity and deliver a reliable customer experience that builds trust.
Watch over your brand: To stay competitive and earn consumer trust, more brands now rely on data insights to make fast, effective decisions that enhance their reputation and boost online sales.
As e-commerce explodes, more leading brands are collaborating with DataWeave for actionable brand analytics to protect their digital shelves, decrease complexity and boost consumer trust. These accurate, trusted insights help brands gain clarity to make smarter e-commerce decisions faster. Making data-driven brand management, promotional and digital marketing decisions helps brands prove their authenticity, improve marketing effectiveness and boost online sales. To see how DataWeave helps brands stand out, sell more and stay competitive, visit www.dataweave.com.
[1] Taylor, Lauren, Chris Biggs, Ben Eppler, Henry Fovargue and Gaby Barrios. How Retailers Can Capture $5 Trillion of Shifting Demand. Boston Consulting Group. August 31, 2020.
[2] Gibbons, David. Ecommerce and content: How retailers have shifted strategies during the COVID-19 pandemic. Digital Commerce 360. August 18, 2020.
[3] US Direct-to-Consumer Ecommerce Sales Will Rise to Nearly $18 Billion in 2020. eMarketer. April 1, 2020.
[4] Wertz, Jia. 3 Emerging E-Commerce Growth Trends To Leverage In 2020. Forbes. August 1, 2020.
[5] Lee, Emma. The incredible rise of Pinduoduo, China’s newest force in e-commerce. TechCrunch. July 26, 2018.
[6] Goyal, Vivek. Browsing e-commerce: An untapped $250B+ opportunity. Medium. September 27, 2020.
[7] Cooper, Paige. 43 Social Media Advertising Statistics that Matter to Marketers in 2020. Hootsuite. April 23, 2020.
[8] Cooper, Paige. 43 Social Media Advertising Statistics that Matter to Marketers in 2020. Hootsuite. April 23, 2020.
[9] Grill-Goodman, Jamie. ‘Digital is the New Normal,’ Nike CEO Says. RIS News. September 23, 2020.
The Great Indian Festival finally arrived and it coincided with Flipkart’s Big Billion Day Sale. The pandemic has pushed consumers to shop online and both, the Great Indian Festival and the Big Billion Day sales had been eagerly anticipated. Flipkart’s sale lasted between 16-21 October, while Amazon’s (in India) took started on 17th October.
It is claimed that Amazon and Flipkart have hit $3.5 billion in sales in just four days. On the last day of its Sale, Flipkart claimed to have achieved 10 times growth as compared to last year’s Big Billion Day sale. Clearly, the sales have surpassed all the forecasts made for this year’s sale. We at DataWeave took a closer look to analyze the discounts that were offered across popular categories, to see if customers really had access to better deals and discounts.
Our Methodology:
We looked at the top 500 products across categories like Fashion – men and women, electronics, Amazon devices, baby products, grocery and personal care. The pricing offered on these products across the sale period was compared with the pre-sale price, to understand the trend in discounts across the popular categories and brands.
The Verdict:
We segmented the products we were tracking into the following:
Type 1: Products were either priced the same or were discounted over the sale compared to pre-sale
Type 2: Products were either priced the same or witnessed price increase during the sale compared to pre-sale
Type 3: Products which saw both price increase and decrease during sale compared to pre-sale
Type 4: Products whose price continued to be the same even during the sale
Flipkart clearly provided the better deals to customers for the categories we looked at during their Big Billion Day sale compared to Amazon. Flipkart discounted 54% of its products during the sale period compared to Amazon, and 26% of the products were discounted.
It is also interesting to note that in addition to offering more discounted products, Flipkart also offered additional discounts than Amazon.
Amazon offered 13.2% additional discount and most of this average discounting can be attributed to a 33.8% discount on Amazon devices. It also ended up increasing the pricing for 16% of the products during the sale period, while Flipkart hiked the pricing for 6% products. 56% of products on Amazon continued being sold at the same price even during the sale.
Additional discounts across product premiumness levels
Premiumness was based on the actual price of a product before the sale event. This was divided into low, medium and high premiumness levels, with high indicating higher selling prices.
In Amazon devices, baby products, electronics and grocery-cooking essentials, Amazon showed a direct relationship between its additional discounts and the level of premiumness. While Flipkart did not seem to follow a particular pattern with respect to product premiumness.
Flipkart offered the highest discounts for premium products in the Fashion category (for both men and women) compared to the rest.
Top brands by additional discounts:
We looked at popular brands across categories to arrive at brands that were being sold at the maximum discount. These brands appeared at least twenty times in the top 500 ranks we considered.
Acer, Philips, Samsung, Lenovo, Bajaj, Asus which were common brands across both Flipkart and Amazon in the electronics category, were being sold at much deeper discounts on Flipkart (almost double), compared to that on Amazon.
Avita was extremely popular under the laptop sub-category on Flipkart and was observed to be discounted the highest during the sale.
In Fashion, Titan was the most discounted brand with 53.9% additional discount but only 4% and 2% of the products offered discounting in mens’ and womens’ fashion respectively. Reebok in mens’ fashion and Fastrack, Sonata and Puma in womens’ wear on Flipkart, had discounts across almost all the products.
In the baby care category, Hasbro gaming on Flipkart had the highest additional discount followed by Funskool. Both the brands had more than 85% of their products discounted.
Johnson’s, which was common on both Amazon and Flipkart, was offered at higher discounts on Amazon compared to Flipkart. However, only 31% products were discounted vs 72% on Flipkart.
Most Visible Brands
We looked at the top 200 ranks across each sub-category to narrow down on the most visible brands across the sale period.
Across all categories and their sub-categories, the sub-category laptop had distinct brands that hold the majority of the products. This is observed both in Amazon and Flipkart where brands like Lenovo, HP, Asus hold more than 33% share of the first 200 products.
“Mobile” category was dominated by brands like Redmi and Boat on Amazon, and Realme and OPPO on Flipkart. These brands occur at least 24% of the time in the top 200 ranks.
Who Won?
There are many ways to look at this. To begin with, the combined sales of Flipkart and Amazon during the festive season in India accounted for more than 90% of the e-commerce industry’s gross sales. That amounts to a 55% year-on-year growth. Delving further, we see that Flipkart was far more aggressive with their offerings.
They discounted 56.8% additional products at an overall discount of 15%. On the other hand, Amazon retained their typical cautious approach to discounting, with only 28.4% of the products, at an overall discount of 12.8%.
If we adopt a more macro view of the sales, we have to take into account that this year is somewhat of an anomaly. Given the social distancing norms and other SOPs governing the common man, more people have been ushered into the world of online shopping. The penetration extended far into the Tier 2 and Tier 3 cities as well, thus potentially benefiting Flipkart, owing to their interior reach.
Going by the numbers, Flipkart seems to have taken this round without a doubt. As we observed though, there are many ways to look at this and what seems to stand out from these two giants, is the consumer. At the end of the day, it’s the consumer that in spite of these strange times, has shopped more than before, indicating that the situation is getting back to a semblance of normalcy.
So Flipkart’s got the sales numbers but the consumer got deeper discounts on more products. As the old adage goes, ‘consumer is king’.
According to our preliminary analysis of Prime Day 2020, Amazon’s rivals offered more generous discounts within Home categories to stay competitive as more consumers invest in their homes this year.
This year the COVID-19 pandemic has transformed consumers into homebodies who increasingly work, learn and shop from home. This year also marks the first time Prime Day took place in the Fall, jumpstarting the holiday sales season.
At DataWeave, we wanted to know whether Prime Day 2020 lived up to the hype and how Amazon’s deals compared to other retailers’ discounts. Our analysis examines products across four popular Home categories: Bed & Bath, Furniture, Kitchen and Pet Care.
Our Methodology
We tracked the pricing of several leading retailers (Home Depot, Target, Walmart and Amazon) selling the Home categories of Bed & Bath, Furniture, Kitchen and Pet Care to assess their pricing and assortment strategies during this annual sales event. Our analysis focused on additional discounts offered during the sale to estimate the true value that the sale represented to consumers. Our calculations compared product prices on Prime Day versus the prices prior to the sale. The sample consisted of up to the top 750 ranked products across 16 popular product types for the home.
The Verdict
Overall, Amazon reported the lowest price reduction in all Home categories (12.4%), compared to Target (22.1%), Home Depot (16.5%), and Walmart (15.1%). Yet Amazon reported the second-highest percentage of additionally discounted products (9.6% vs. 11.0% for Target).
After Prime Day ended, certain retailers’ Home assortments saw more significant price increases than others. For instance, 88% of Target’s 760 products in Bed & Bath, Furniture, Kitchen and Pet Care received a price increase during the post-sale period, compared to 47% of Walmart’s 1005 products. Walmart’s everyday low price strategy helps to explain the difference between the two big box retailers.
These results suggest that Prime Day 2020 may boost Amazon’s marketing and PR engagement yet its rivals offered the most generous deals in Home categories. As home-related categories’ sales soared during the pandemic, Amazon’s competitors offered deep discounts to stand out online and grow their market share. As such, consumers may want to embrace the habit of comparing multiple retailers’ websites to discover the best Prime Day deals in Home categories.
Top product types by additional discount
In Bed & Bath, Target offered the biggest average additional discount (27.4%) and Amazon offered the lowest (13.3%). Bed sheets and pillowcases were a popular product category for additional discounts across all four retailers, with Target offering the best average additional discount at 31.3%. Other popular product types among rival retailers included blankets, comforters and bathroom furniture.
In Furniture, Home Depot (20.5%) offered the biggest overall additional discount, closely followed by and Target (19.2%). Living room furniture was a popular subcategory for all four retailers, with Home Depot offering the highest additional discount (29.1%). Other popular product types included furniture for the bedroom, home office, kitchen and dining room.
In the Kitchen category, Target offered the biggest average additional discount for small appliances (21.8%), a subcategory in which all four retailers offered discounts. Within the large appliance subcategory, Walmart’s additional discounts were nearly triple Amazon’s (15.7% vs. 5.6%).
Within the Pet Care category, Target offered the biggest average additional discount (18.5%). Cat food was a popular product category, with Target offering the best average additional discount (50.0%). Other popular product types across all four retailers included dog collars, leashes and dry dog food.
Additional discounts across product “premiumness” levels
Premiumness was calculated as the average selling price before the sale event. This was divided into low, medium and high premiumness levels, with high indicating higher selling prices.
In Bed & Bath, most retailers showed an inverse relationship between their additional discounts and the products’ level of premiumness. Target offered the biggest additional discounts across all levels of premiumness, more than double Amazon’s discounts (27.2% vs. 12.3%). Target’s bold discounting strategy shows a commitment to protecting its competitive position across the entire Bed & Bath category.
By far, Amazon offered the greatest percentage of additional discounts in Bed & Bath compared to its rivals across all levels of premiumness. Comparatively pervasive discounts help the e-commerce giant offer a greater variety of appealing deals within this category.
In Furniture, most retailers showed a direct relationship between their additional discounts and the level of premiumness. Notably, Home Depot offered massive additional discounts at the high premium level, nearly triple Amazon and Walmart (34.5% vs. 12.7%). This move suggests Home Depot is serious about winning the business of upscale consumers in the Furniture category.
Target differentiated its assortment by discounting by far the greatest portion of its Furniture at all premiumness levels (22.4%) and Home Depot discounted the least (4.4%). Amazon and Walmart distributed the greatest portion of their additional discounts to the moderate level of premiumness. Target’s strategy tries to attract all Furniture shoppers while Amazon and Walmart try to make their mid-market offerings affordable to more consumers.
Across all levels of premiumness for Kitchen products, Target offered the biggest additional discounts, including almost double Amazon’s discounts at the medium level (22.5% vs. 13.4%). Target’s aggressive discounting shows a desire to be more competitive by attracting consumers at all levels of the Kitchen category.
In the Kitchen category, most retailers offered a direct relationship between the proportion of additional discounts and the level of premiumness, yet Home Depot showed an inverse relationship. Amazon’s proportion of additional discounts across all levels of premiumness nearly tripled Home Depot’s (10.1% vs. 3.7%). This discount strategy shows Amazon’s willingness to offer shoppers deals across a broader variety of Kitchen items.
In Pet Care, Walmart offered the highest overall additional discounts (16.2%), which could fortify its low-cost leadership position for pet lovers at all price points.
While Target offered the greatest overall percentage of additional discounts in Pet Care, Amazon applied more discounts to the higher end of the premium spectrum and Target focused on the lower end.
Additional discounts across visibility levels
In Bed & Bath, Target offered the highest overall additional discounts across all levels of visibility (27.3%) and Amazon offered the lowest (12.4%). Amazon focused its additional discounts on the most visible Bed & Bath products to help online shoppers discover those items with ease and make them appealing enough to add to their cart.
Amazon offered the lowest additional discounts in the Furniture category across all levels of product visibility. Yet, among the Furniture category’s most visible items, Amazon offered its highest additional discounts. Home Depot’s additional discounts approach was the most aggressive except among the lowest product visibility levels. Home Depot’s discount strategy shows a desire to compete for Furniture’s most visible items.
In the Kitchen category, Home Depot consistently offered the lowest additional discounts among products at the higher visibility levels. Conversely, Target was the most aggressive in this category, offering additional discounts of up to 43.2% at moderate levels of visibility and double Home Depot’s discounts (26.3% vs. 13.4%) among the most visible items. Amazon may feel confident that men already choose Amazon for their apparel needs.
In Pet Care, the retailers generally offered the most additional discounts for items in the middle of the visibility spectrum. Walmart offered the most aggressive additional discounts among the most visible Pet Care items, more than double Target’s discounts (13.5% vs. 6.5%).
Overall, Prime Day 2020 offered an ideal time for Amazon to attract homebound consumers to invest in domestic products, yet its rivals offer much higher additional discounts in Bed & Bath, Furniture, Kitchen and Pet Care. How about other categories? Watch this space for more insights!
Our preliminary analysis reveals that Prime Day 2020 motivated Amazon’s rivals to offer deeper discounts in key categories to try to make their merchandise more magnetic and lure consumers away from the e-commerce giant.
This year’s Prime Day is momentous, as the COVID-19 pandemic has encouraged more consumers to make online shopping a more regular habit. It also marks the first time Prime Day took place in the strategically significant final quarter of the year, kicking off the holiday sales season.
At DataWeave, we wanted to know whether Prime Day 2020 lived up to the hype and how Amazon’s deals compared to other retailers’ discounts. Our analysis examines products across three popular categories: electronics, beauty and fashion.
Our Methodology
We tracked the pricing of several leading retailers (Best Buy, Target, Walmart and Amazon) selling consumer electronics, beauty and fashion to assess their pricing and assortment strategies during this annual sales event.
Our analysis focused on additional discounts offered during the sale to estimate the true value that the sale represented to consumers. Our calculations compared product prices on Prime Day versus the prices prior to the sale. The sample consisted of up to the top 750 ranked products across 21 popular product types in consumer electronics, beauty and fashion.
The Verdict
Overall, Amazon reported the lowest price reduction in the Electronics, Beauty and Fashion categories (13.4%), compared to Best Buy (22.5%), Target (21.7%) and Walmart (16.3%). Yet Amazon reported the second-highest percentage of additionally discounted products (12.0% vs. 15.7% for Target).
After Prime Day ended, certain assortments reflected more significant price increases than others. For instance, 97% of Target’s 158 products in Electronics, Beauty and Fashion had a price increase during the post-sale period, compared to 49% of Walmart’s 986 products. This discrepancy makes sense given Walmart’s everyday low price strategy.
These results suggest that although Prime Day generates tremendous media buzz for Amazon, the most generous deals come from its rivals. To stand out and lure shoppers away from Amazon, competitors offered comparatively deeper discounts, especially in categories in which they want to grow their market share. This means online shoppers would be wise to compare prices across retailers’ websites to find the best cross-category deals on Prime Day.
Top product types by additional discount
In Electronics, Best Buy offered the biggest average additional discount (22.4%) and Amazon offered the lowest (9.4%). Tablets were a popular product category among Amazon, Best Buy and Walmart, with Best Buy offering the best average additional discount at 19.1%. Other popular product types among rival retailers included TVs, desktops and laptops.
In Beauty, Target (13.2%) and Walmart (13.1%) almost tied for the biggest overall additional discount. Makeup was a popular beauty subcategory, with Walmart offering the highest additional discount at 19.7%. Other popular product types included hair care, skin care and fragrance.
In Men’s Fashion, Target offered the biggest average additional discount of 28.1%. Suits and blazers were a popular fashion subcategory, in which Target offered the highest average additional discount at 50.0%. Other popular product types included T-shirts and tank tops, shirts and jeans.
Within the Women’s Fashion category, Walmart offered the biggest average additional discount of 20.5%. Tops and tees were a popular product category across all three fashion rivals, with Walmart offering the best average additional discount at 23.6%. Other popular product types included dresses, jumpsuits and jeans.
Additional discounts across product “premiumness” levels
Premiumness was calculated as the average selling price before the sale event. This was divided into low, medium and high premiumness levels, with high indicating higher selling prices.
In Electronics, Amazon showed a direct relationship between its additional discounts and the level of premiumness; Best Buy and Walmart showed an inverse relationship. Best Buy offered the biggest additional discounts across all levels of premiumness, nearly triple Amazon’s discounts (20.7% vs. 7.0% ) at the low end of the premium spectrum, and more than double Amazon’s discounts (18.5% vs. 7.3%) at the moderate level. Best Buy’s discounting strategy show it’s serious about protecting its competitive position in electronics.
Best Buy and Walmart offered the most additional discounts at the high end of the premiumness spectrum, making both retailers more competitive in the high-ticket electronics category. By contrast, Amazon offered nearly double the additional discounts of its rivals within the low segment, which helps to protect its margins while making products even more affordable and appealing.
In Beauty, Amazon and Walmart offered their biggest additional discounts at the low premium level, possibly to position those products as loss leaders. Meanwhile Target nearly doubled and tripled its rivals’ additional discounts at the high premium level (30.0% vs. 16.0% for Walmart and 11.0% for Amazon) to stand out in this intensely competitive category.
Amazon stood out by discounting the greatest portion of its Beauty offerings at all premiumness levels and Target discounted the least. Amazon and Walmart showed a direct relationship between their distribution of additional discounts and the beauty products’ premiumness level.
Across all levels of premiumness for Men’s Fashion, Target offered the biggest additional discounts, including more than triple Amazon’s discounts at the high end (38.4% vs. 12.4%). Target’s aggressive discounting shows a desire to be more competitive within the most premium segment of Men’s Fashion.
Amazon’s additional discounts accounted for the greatest percentage of its Men’s Fashions across all levels of premiumness, nearly triple Target’s overall average (15.4% vs. 5.3%). This approach shows Amazon’s willingness to give shoppers deals across a broader variety of Men’s Fashion items.
In Women’s Fashion, Target’s and Walmart’s overall additional discounts were comparable, and Amazon’s discounts were consistently the lowest among all levels of premiumness. Walmart offered its most generous discounts at the low and medium level of premiumness, which could reinforce its low-cost leadership image.
While Amazon and Target offered a comparable overall percentage of additional discounts in Women’s Fashions, Amazon applied more discounts to the higher end of the premium spectrum and Target focused on the lower end.
Additional discounts across visibility levels
In Electronics, Amazon offered the lowest average additional discounts across all levels of visibility. Among the most visible electronics, Amazon and Best Buy gave the most visible electronics higher additional discounts to make those items more alluring to help consumers find the items fast and add them to their online baskets.
Among the Beauty category’s most visible items, Amazon and Target offered their highest additional discounts. Yet Target was most aggressive in beauty, offering a 30% additional discount at the most visible end of the spectrum as well as at the least visible. This discount strategy shows Target wants to compete in Beauty, spreading its generosity beyond an exclusive focus on highly visible items.
In Men’s Fashion, Amazon consistently offered the lowest additional discounts at all visibility levels. Target was the most aggressive in this category, offering additional discounts of 50% at moderate levels of visibility and 34.5% among the most visible items. Amazon may feel confident that men already choose Amazon for their apparel needs.
In Women’s Fashion, the retailers generally offered the most additional discounts for items at the higher end of the visibility spectrum. Walmart offered the most aggressive additional discounts among the most visible items in Women’s Fashion to try to boost its market share in this category.
Overall, while Prime Day is an effective way for Amazon to boost brand engagement, its rivals overwhelmingly offer higher additional discounts in Electronics, Beauty and Fashion. How about other categories like the booming Home space? Watch this space for more insights!
This year, homebound consumers crave the convenience of food delivery. Growing 20% since 2015, restaurant delivery has sparked intense rivalry to reach consumers’ homes. Although the pandemic led to $165 billion in lost sales industry-wide between March and July, experts predict online food delivery sales will reach $220 billion by 2023, accounting for 40% of total restaurant sales.[1,2]
This massive market opportunity makes food delivery an urgent priority for restaurants to stay competitive and solvent during the pandemic. This year nearly one in six U.S. restaurants have closed either permanently or long-term.[3]
Also, 40% of U.S. operators say they will likely be out of business within six months if economic conditions persist and 60% of Canadian restaurants could close permanently by November.[4,5]
COVID-19 compounds market complexity
Powerful market trends are rattling restaurants. During the pandemic, nearly 70% of operators have added third-party delivery to lift sales.[6]
This year, third-party delivery from food delivery apps like Uber Eats, Grubhub and DoorDash will grow 21% over 2018.[7] The global market for cloud kitchens (also called ghost kitchens or virtual kitchens), commercial kitchens intended for delivery-only orders, will grow from $650 million in 2018 to $2.6 billion by 2026.[8]
To avoid the need to rely on delivery partners, many chains invest in their own last-mile delivery capability to serve their fleet of restaurants. E-grocery sales are poised to surge 40% in 2020 and meal kits have boomeranged back into popularity, nearly doubling 2019 sales.[9, 10]
Consumers demand speed to keep their food fast, fresh and hot. Prompt service matters, as one survey found when consumers face a food delivery issue, 93% want it resolved within 10 minutes.[11]
The recession and job losses mean more consumers now need affordable food options. Meanwhile, restaurants are investing more in technology to modernize operations for efficient omnichannel service.
How restaurants are adapting to 2020’s disruption
Restaurant prices have risen during the pandemic to cover operating costs. Third-party delivery fees have led 41% of consumers to prefer to order food by contacting the restaurant directly (vs. 16% for third-party delivery).[12] To optimize pricing competitiveness, more restaurants now compare their delivery fees and offerings with rivals’ to spot and correct gaps, and keep their prices affordable.
To streamline operational processes and costs during the pandemic, 28% of restaurants shrank their menus.[13]
For clarity on which items to keep, operators now use data insights on restaurant listings and menu items down to the ZIP code level. This information also helps them decide whether to adapt to consumers’ diverse tastes, including vegan, gluten-free and organic, for competitive local assortments.
Outperform rivals: Restaurant operators seek proof of their brand visibility on food delivery apps’ homepages.
Restaurants have discovered consumers welcome reasons to celebrate at home this year. One chain’s weekly virtual happy hours on Facebook Live drew 80,000 participants and a $40,000 sales increase from delivery and takeout orders.[14]
More restaurants now compare their promotional strategies with rivals’ to evaluate marketing performance, including homepage discoverability and visibility ranking, to ensure consumers find their brand online with ease.
Delivery speed and precision also matter. A survey found 70% of consumers had food delivery order complaints, including late delivery (50%), incorrect order (37%) and cold or stale food (36%).[15] Using accurate geographic data can help restaurants improve speed and the customer experience.
To gain a competitive advantage in today’s booming food delivery market, a growing number of leading chains and food delivery providers are collaborating with DataWeave to access actionable insights to make better strategic and operational decisions faster. Using trusted insights to make data-driven pricing, menu and promotional decisions help restaurants save time, reduce risk and gain clarity in today’s evolving market.
Applying DataWeave’s accurate, up-to-date information also helps restaurants deliver affordability, convenience and variety to remain responsive to consumers and agile among competitors. To see how DataWeave helps restaurants stay relevant and competitive, contact us today.
[1] Rogers, Kate. Winter is coming, bringing a new challenge to already-struggling restaurants. CNBC. September 14, 2020. [2] Zahava Dalin-Kaptzan. Food Delivery: Industry Trends for 2020 and beyond. Bringg. April 30, 2020. [3] Klein, Danny. 100,000 Restaurant Closures Expected in 2020. QSR. September 14, 2020. [4] Rogers, Kate. Winter is coming, bringing a new challenge to already-struggling restaurants. CNBC. September 14, 2020. [5] Charlebois, Sylvain. Don’t Want to Save the Restaurant Industry? Fine, but Use it to Save the Canadian Economy. Retail Insider. September 11, 2020.
[6] Rogers, Kate. Winter is coming, bringing a new challenge to already-struggling restaurants. CNBC. September 14, 2020.
[7] US Food Delivery App Usage Will Approach 40 Million Users in 2019. eMarketer. July 2, 2020.
[8] Levy, Ari. Virtual Kitchen, founded by ex-Uber execs to help restaurants with delivery, raises $20 million. CNBC. Sept. 8 2020
[9] Redman, Russell. Online grocery sales to grow 40% in 2020. Supermarket News. May 11, 2020.
[10] De Leon, Riley. How the coronavirus pandemic delivery surge created a lifeline for Blue Apron meal kits. CNBC. May 22, 2020.
[11] Guszkowski, Joe. Delivery services have room to improve, consumers say. Restaurant Business Online. Sept. 1, 2020.
[12] Guszkowski, Joe. Consumers’ desire to order directly from restaurants is a big opportunity. Restaurant Business Online. Aug. 27, 2020.
[13] Romeo, Peter. Best practices for weathering a second COVID wave. Restaurant Business Online. Aug. 28, 2020.
[14] Ibid.
[15] Guszkowski, Joe. Delivery services have room to improve, consumers say. Restaurant Business Online. Sept. 1, 2020.
As DataWeave continues to engage with brands and manufacturers of all sizes, a consistent theme keeps emerging, “click and collect tracking”. Right now, brands rely on manual-store checks or waiting upwards of two weeks for a retailer to report sales data, which reveals low sales because a product is out of stock. In addition, there are always questions about the local price of your products compared to top competitors in the category. This is where DataWeave’s CPG Brand Monitor solution can help.
On a daily basis, we track over 13,000 variant level SKUs across 100 stores, via seven of the top grocery retailers. We have selected the largest grocers in each region of the US, to allow for the widest coverage. These grocers include Albertsons/ Safeway in the west, HEB in Texas, Kroger in the upper mid-west, Wegmans in the Mid-Atlantic and Publix in the Southeast.
How does it work?
In the application, you will see the list of all the SKUs we’re covering, with filters on the left side of the page to help with navigation. You can sort by Brand, Category, Store/ City, State, etc. After the filters are applied, the SKU list will be displayed based on these filters. On the right side of the screen, you will see all the product level details including a 7-day price history, individual store level pricing/ stock availability and exportable charts and graphs.
How do I get access?
Simply access the CPG Brand Monitor page, fill in your credentials via “Start Free Trial” and your login will be sent directly to your inbox. No commitments or phone calls are needed to test out the data. After a few days, our team will be in touch to make sure you understand how to navigate the tool and take you through our subscription options.
What else do we offer?
DataWeave also offers a full Digital Shelf Analytics suite that covers Share of Voice (keyword, navigation and banner audits), Content Audit/ Optimization, Ratings/ Review Sentiment Analysis, Promotional Analysis, and much more.
JioMart, the online channel for Reliance Retail Limited, launched in December 2019 as a contender in the e-grocery segment. Currently in India, this segment is being dominated by bigbasket, Amazon, Flipkart Supermart, Grofers, etc. After less than a year and from their initial launch in Mumbai, they now have their presence in 205 cities across India.
According to their recent press release, they claim to be clocking over 250,000 daily orders, compared to bigbasket’s 220,000 and Amazon’s 150,000. To get an understanding of this rapid penetration, we had a look at the PIN codes that JioMart serves, spanning the country.
The map below represents the percentage of PIN codes that are being served by JioMart’s online grocery in each state:
**Disclaimer -Map for representation purposes only
While states like Chandigarh, Delhi and Punjab in the North are covered extensively, JioMart has a stronger distribution in the Southern states.
The image below shows the top ten states in India where JioMart’s online grocery has the highest presence:
They’re yet to launch in 14 more states but it’s interesting to note that in this limited time, they’ve managed to cover 14% of the PIN codes in the country and all this, in the midst of lockdowns.
Assortment
To get an idea of the assortment in their range, we analyzed select PIN codes across three tiers of cities in India. The parameters we looked at were categories, brands and discounts to get an understanding of how JioMart is stacking up against its competitors. The cities we examined were:
Tier 1 – Bangalore, Delhi, Kolkata, Mumbai
Tier 2 – Ahmedabad, Jaipur, Kochi, Visakhapatnam
Tier 3 – Mohali, Mysore, Nagpur, Siliguri
In its range, they offer eight broad categories, of which, we focussed on the four that offer the highest selection of products: home care, personal care, snacks & branded food and staples.
The table below represents the average selection of products offered across each tier.
Overview of discounts offered and the private label split
Out of the assortment we looked at in the three tiers, we noticed that an average of 18% of the products are JioMart’s private labels. What stood out further is that private labels accounted for 48% in the Staples category and 24% in Personal Care. We noticed this trend (increase in the private label) when we did an analysis of Amazon.
When it comes to discounts, we noticed that a near-total 91% of the products listed are being sold at a discount. Out of this, the highest discounts were witnessed in the Home Care and Staples categories.
The brands with the highest number of products listed were Good Life, Reliance, Amul, Gillette and items sold loosely. All these accounted for 14% of the assortment. Out of these, Good Life, Reliance and the loose items are JioMart’s private labels.
Competitor analysis
To get an idea of where JioMart stands with relation to its competitors, we focussed on food and essentials in the Tier 1 cities. The table below highlights the number of product offerings in each category:
It’s clear that in these categories (food and essentials), JioMart has the least number of products on discount. There’s no doubt that bigbasket is miles ahead in its product range/ assortment.
To get a better idea of the discounting patterns, we analyzed the same categories to get a count of the number of products being discounted, as well as the average discount being offered.
We noticed that JioMart bookended our analysis – the least average discount, across the most number of products. Grofers offered the highest average discount of 23% with Flipkart Supermart and bigbasket closely behind. Lastly, bigbasket had the least number of products on discount with a little over 53%.
Conclusion
JioMart launched during a tumultuous and unprecedented time; the COVID-19 pandemic and the subsequent nation-wide lockdowns. Given this trial by fire, they managed to make an impact in this highly competitive space. Their expansion plans of tying up with mom and pop stores to fortify their penetration, had to take a back seat due to the ongoing situation but is sure to resume once conditions improve. This set-back did not however deter JioMart from attracting strategic investments from Facebook, Google and 12 other investors in a span of 3 months.
In a study by Goldman Sachs, it found that India’s e-commerce business is expected to grow at a compound annual growth rate of 27% by 2024, resulting in a $99 billion market share. What’s even more shocking is that 50% of this market will be captured by Reliance Industries. It, therefore, stands to reason that all we’ve seen and heard of so far, is merely the tip of the iceberg and there’s surely more to come in the near future.
We’re thrilled to announce that we have teamed up with Kenshoo to offer an integrated marketing solution that combines DataWeave’s digital shelf analytics and commerce intelligence platform with Kenshoo’s ad automation platform. This in turn, provides better recommendations on promotions to retailers and consumer brands.
As e-commerce surges, consumer brands can now promote their products through retail-intelligent advertising. Product discoverability, content audit, and availability across large marketplaces can be critical to a brand’s success. Using DataWeave’s digital shelf solutions, Kenshoo now can offer marketers greater visibility into a brand’s performance.
Even large retailers and agencies can use our commerce intelligence platform to improve their price positioning, address category assortment gaps, and more.
Through this partnership, Kenshoo – a global leader in marketing technology, can help its significant base of consumer brands and retailers invest their marketing dollars intelligently and in a timely manner.
At DataWeave, we have constantly strived to bring in a holistic approach to help our customers optimize their online sales channels. This partnership furthers our resolve in this direction. As we collectively strive to adjust to a post-COVID-19 world, we are observing an acceleration towards digital commerce. This acceleration and change in consumer behavior is going to be a lasting change, creating significant growth opportunities for both DataWeave and Kenshoo.
With this partnership, we look forward to helping our customers make timely, intelligent, and data-driven decisions to grow their business.
Among Amazon’s most prominent and decisive steps in achieving retail dominance over the last few years has been its focus on expanding its private label portfolio.
The most recent collaborative report between DataWeave and Coresight Research determines that Amazon’s private label assortment in early 2020 has grown three-fold over the previous two years, most of which is in categories outside of apparel and accessories.
In addition, the report covers various facets of Amazon’s private label penetration and strategy. These include the size of Amazon’s private label portfolio, the distribution of private label products by category, the product ratings and number of reviews, the average price points across products and brands, and more.
Our detailed and proprietary Amazon private label dataset includes information on over 20,000 products and 111 brands.
Around half of the private-label products are in clothing, footwear and accessories, which is lower than the three-quarters found in our similar research from June 2018, indicating Amazon’s push into a broader range of categories.
The average Amazon private-label product generates a customer rating of 4.3 out of 5, representing positive customer feedback overall.
The number of private-label products—22,617—is more than triple the total of 6,825 from June 2018 (see our previous report). The number of private-label brands also increased substantially (up 50% versus June 2018), indicating that the e-commerce giant has stepped up its private-label strategy.
Around Half of Private-Label Products Are in Clothing, Footwear and Accessories
Just over half of Amazon’s private-label products are in “clothing, footwear and accessories,” versus almost three-quarters when we undertook similar research in June 2018, indicating Amazon’s push into a broader range of categories. Other categories that feature more than 1,000 private-label products include “home and kitchen,” “grocery and gourmet food” and “tools and home improvement.”
Source: DataWeave/Coresight
The Average Amazon Private-Label Product Generates a Customer Rating of 4.3 out of 5
We examined feedback provided by Amazon’s private-label customers: Customer satisfaction can be measured by the average star rating that customers have left in reviews. We chart both average star rating and average number of customer reviews per product in the graph below.
The average Amazon private-label product generates a customer rating of 4.3 stars out of 5, suggesting overall solid customer satisfaction levels.
Source: DataWeave/Coresight
The full report is available for Coresight’s premium subscribers. It includes further details of categories and subcategories that suggest longer-term implications—including how Amazon targets a niche customer base through specific category labels but appeals to broader consumer needs by offering multicategory labels.
To access DataWeave’s proprietary database on Amazon’s private label brands and products, reach out to us today!
For today’s shoppers, instant gratification is the need of the hour. It’s, therefore, no surprise that furniture e-retail has been picking up steam over the last decade. What was once a norm to physically touch and feel before making a purchase, is now just a few clicks away. Retailers have bridged the gap by making the purchase process as seamless as possible – easy finance options, hassle-free returns and variety.
While several factors play a role in driving consumers to shop furniture goods online, price is the primary motivator, as is the case with most popular product categories sold online.
During Black Friday 2019, DataWeave performed an analysis on a sample of 23,000+ products across six of the top furniture retailers – Amazon, Home Depot, JCPenney, Target, Walmart and Wayfair. Ten product types were covered in the furniture category (such as Beds, Bookcases, Mattresses, Sofas, etc.) and the analysis focused on the top 500 ranked products of each product type.
To get an accurate depiction of the additional markdowns during the sale, we took the mode of the prices for the preceding week and compared them with that during the sale.
Additional markdowns
Target (25%) and Home Depot (21%) marked down their prices most aggressively during the sale. JCPenney and Wayfair stood out for offering additional markdowns on the highest portions of their ranges (67% and 46% respectively), even though the average markdown percentage was fairly conservative. Amazon and Walmart were steady as usual, with additional markdowns of 8% and 10% on 15% and 17% of their assortment, respectively.
Premiumness
To further understand the furniture pricing strategies of these retailers, we categorized their products into buckets of how expensive or cheap the product is (High, Medium, and Low in terms of price), relative to the rest of the products hosted by the retailer, and studied how the additional markdowns varied across these buckets. Where the MRP was not displayed, the most expensive price of the product during the holiday period prior to Black Friday was considered to define the “premiumness” of the product.
Two patterns clearly stand out from this analysis. Most of the retailers remained relatively equitable between their premium categories with nothing significant to report in terms of varying markdowns. Home Depot and and JCPenney are the only exceptions here, but not by much. The other interesting insight is that the percentage of marked-down products had a near unanimous pattern of the high level being the most covered, followed by the medium and then low.
Therefore, while there wasn’t a significant variation in the average markdown across premiumness levels, a larger portion of the high-premium goods were consistently offered at a discount across all retailers.
Popularity
Much like our premiumness categorization, we investigated products based on their popularity levels as well. We’ve defined popularity using a combination of the average review rating and the number of reviews for each product, condensed to a scale of low, medium and high.
We observe slightly different furniture pricing strategies adopted by retailers across popularity levels. While Home Depot, Amazon, and Wayfair chose to provide higher markdowns on their more popular products, Target, JCPenney, Walmart chose to provide higher markdowns on their least popular products. In addition, a larger portion of the least popular products were consistently offered on discount by almost all retailers.
In combination with our findings across premiumness levels, we can surmise that part of the strategy of most retailers was to liquidate their stock of expensive and unpopular products during the sale.
Price Change Activity
As part of our analysis, we also tracked the level of pricing activity across retailers over the last week of November, in terms of the number of price changes made as well as the average price variation for each retailer.
In general, we can see that Amazon and Walmart consistently made several price changes through the week, though the average magnitudes of these price changes were relatively low. This echoes the pattern we’ve observed through our analysis of other product categories during the sale event, as well.
Also, we see an almost coordinated increase in the number of price changes and the average magnitude across the 27th and 28th of November. This is likely an attempt by the retailers to get a head start on Black Friday deals.
An unusual and interesting pattern was observed with Wayfair, which started out the week with the most changes at 2500. It then tanked the next day and hovered around 500 till the 28th, only to spike to 2500 again. All these changes though, had their variation in and around 5%.
In summary, its interesting to observe how different retailers approached the much-anticipated holiday season sale events differently. As one might expect, there are significant variations among retailers in the aggressiveness of discounting activity as they approached Black Friday, and on Black Friday itself. Contrasting pricing strategies for popular and premium goods were also observed.
If you would like to learn more about the pricing of top U.S. retailers across other product categories like consumer electronics, fashion, and beauty & health, check out our series of articles on Black Friday 2019.
We’ve come a long way from face paint and medicinal herbs to multi-billion dollar industries revolving around health and beauty. Customers are getting increasingly bombarded with variety that promises something for everyone. In fact, a recent DataWeave study identified Health & Beauty as one of the most popular CPG categories in the U.S., both in terms of assortment strength and brand concentration. As with most other categories, pricing activity around Health & Beauty is especially abuzz when Thanksgiving weekend comes around.
As part of our series of articles analyzing the pricing of leading retailers across categories on Black Friday, the DataWeave team performed an analysis on a sample of 14,000+ products across six top retailers – Amazon, JC Penney, Macy’s, Nordstrom, Target and Walmart. Seven product types were covered across the category, such as Fragrance, Hair Care, Makeup, etc. and the analysis focused on the top 500 ranked products of each product type.
Additional markdowns
For this analysis, we considered the mode of the prices for the week before and compared it with that during the sale. This painted a picture of the additional markdowns for the duration of the sale.
Similar to our prior coverage of the Fashion category during Black Friday, Macy’s had the broadest reach in terms of the marked down products at 25.6%. The average percentage of the markdowns was 22% and was only eclipsed by JC Penney with an average of 34.7%, though this was only offered on 3% of its range. At the other end of the spectrum, Amazon and Walmart had the lowest markdowns at 8.9% and 8.4% respectively but were among the top three in products covered (18% & 12%). Target and Nordstrom offered mid-range markdowns across the board but on a rather conservative selection of products of 5% and 3%, respectively.
Additional markdowns by product types
When we delved further into the product types, we noticed that a majority of the retailers heavily marked down makeup, shampoo & conditioner and men’s hair care products. The table illustrates the top three discounted categories for each retailer we analyzed.
Premiumness
We categorized the products across retailers into buckets of how expensive or cheap a product is, relative to the rest of the products hosted by the retailer in the respective product type. Where the MRP was not displayed, the most expensive price of the product during the holiday period prior to Black Friday was considered for this categorization. We then tagged products as High, Medium and Low in terms of product premiumness, with High referring to the more expensive products.
In line with previous trends, Macy’s had the highest markdown on its high level products at 32.8%. It also had the widest coverage for the category at 20%. Amazon, Macy’s, Target, Walmart followed the expected approach of providing higher markdowns on the more premium products, and also on a higher portion of these products. This would be consistent with their goal of providing attractive offers on premium goods while also protecting their margins.
JC Penney and Nordstrom were exceptions here, with JC Penney providing higher markdowns on its cheaper goods, while Nordstrom focused its markdowns on the medium bucket. That being said, it should be reiterated that the portion of products with markdowns for both thee retailers was relatively small.
Popularity
Similar to categorizing the products at levels of product premiumness, we categorized them into levels of popularity as well. Here, popularity is defined using a combination of the average review rating and number of reviews obtained for each product.
Interestingly, no consistent pattern has emerged that indicates a strategic focus on factoring product popularity into their pricing strategies for Black Friday.
Macy’s, JC Penney, and Nordstrom chose to provide higher markdowns on their highly popular products, of which only JC Penney and Macy’s chose to also markdown a higher portion of their highly popular products. It was just as common though to see retailers (including Amazon) marking down the prices of their least popular products. This is likely an attempt by the retailers to liquidate their excessive stock of less popular products during the sale.
Price Change Activity
As documented quite often in recent years, the Black Friday sale is no longer limited only to a single day, but attractive offers are often seen right through November, especially over the last week of the month. We tracked the level of pricing activity across retailers over the last week of November, in terms of number of price changes as well as the average price variation for each retailer.
In typical fashion, we observed that Amazon had the most number of pricing changes by a large margin, peaking at 2500 for the set of products tracked. The next in line was Walmart a long way down at 618 changes on the 27th. Even after the multiple changes, their average price change variation remained at the lower end of the scale – in and around 10%.
The rest of the retailers exercised fewer price changes, with the slight exception of Macy’s in the days leading up to Black Friday. However, the changes almost ceased from the day before only to marginally rise on the 29th.
While all the retailers tended to follow a predictable pattern of decreasing variation on the 28th and sharply increasing it the next day, Nordstrom and Walmart did the exact opposite, having likely chosen to jump the gun in offering discounts during Black Friday.
Conclusion
To conclude, we deduced that Macy’s had relatively higher markdowns on more of its products than the rest. JC Penney, Nordstrom and Target offered high markdowns on the face of it but on a very small section of products. Unsurprisingly, Amazon and Walmart stayed true to their past patterns and remained conservative in their additional markdowns during the sale but generous in their reach.
Have a look here at our other observations regarding the Black Friday sale and stay tuned for more insights from our analysis of other product categories!
Online holiday shopping (Nov-Dec) in the US for 2019 is projected to be $143.7B, a 14.1% increase from 2018. This sets a rather exciting stage for retail giants in the battle to claim market share. Interesting patterns emerge as each one tries to out-smart the other. Black Friday, in particular, is when most of the activity was expected to be concentrated.
Inevitably, consumer electronics had strong representation, according to research by Coresight. As traffic steers more towards online shopping, there’s an increased sense of comfort in purchasing big ticket items on an ecommerce platform. There are multiple reasons why electronics lead the race during the holiday season – easy to gift, personal indulgence, comparatively shorter shelf life and well, because who among us can really resist a gadget on sale.
In line with expectations during the season, there’s been a slew of generous discounts across the board. According to prior trends, Amazon was on course to be the lowest priced. In order to assess this, we decided to study a sample of 1000 products on Amazon and match them against its competitors like Walmart, Target, Best Buy and New Egg. Doing this gave us an accurate picture of the comparative pricing across retailers during this season, right up to Black Friday.
Competitive Pricing Analysis
There is a commonly held assumption that Amazon is the lowest priced retailer in most cases. How true is that? Here are our findings:
We tracked the split across three scenarios during the holiday period – Amazon being exclusively the lowest priced, Amazon sharing the lowest priced spot and Amazon not being the lowest priced.
Clearly, Amazon monopolized the share of lowest priced products during the entire period – with its share of lowest priced products ranging between 86% and 60%. The dip from 86% to 60% was immediate on the 27th, as Amazon’s competitors caught up. In general, Amazon’s share of lowest priced products fell from 76% to 62% on Black Friday, as its competitors launched their most aggressive promotional campaigns for the holiday season. As shown in the next chart below, a large portion of this can be attributed to Target’s pricing activity.
Relative Price Index
From 21 November until Black Friday, we calculated the price index across retailers, which indicates the relative pricing levels each day for the set of matched products – the lower the price index, the lower the average relative price.
Unsurprisingly, Amazon has been consistently the lowest priced by a fair margin. A few rungs down, New Egg and Fry’s have been going head-to-head with their price positions. Target on the other hand, underwent a spike in relative pricing from 26-28 November. To sum up, in order of lowest pricing, it’s Amazon, Best Buy, Walmart, New Egg, Fry’s and Target.
Additional Markdowns
While the insights above were unearthed by comparing the products of retailers against a sample of 1000 Amazon products, we went further and performed a separate analysis on a different sample of 15,000+ products across retailers, which focussed on the top 500 ranked products of each product type for Amazon, Best Buy, Target and Walmart. The product types considered include Digital Cameras, DSLRs, Headphones, Laptops, Mobile Phones, Refrigerators, Tablets, Televisions, USB Flash Drives and Wearables.
Here, we compared the prices during the sale with the mode of the prices of the same retailer the week before. This put into perspective the level of additional markdowns during the sale period, enabling us to better understand the additional value to shoppers during the sale period (since discounts are often offered during non-sale periods too).
Looking at opposite ends of the spectrum, we find Amazon with the least drastic markdowns during the sale as it tends to consistently have lower prices across the board. At the other end, there’s Best Buy and Target with the most aggressive markdowns; Target taking the lead, 25.5% on 35% of its products, which is also consistent with the activity we observed in the previous sample of matched products.
Going further, we’ve broken down the markdown activity by the top product types for each retailer. Across the board, we observe attractive discounts on Headphones, USB Flash Drives and Mobile Phones.
Price Change Activity
With the proliferation of pricing intelligence tools (often driven by algorithms), dynamic pricing is a commonly observed behavior among retailers. We analyzed this trend during the holiday period to identify the retailers that are most aggressive in their price change activity. The following charts reveal the number of price changes performed by retailers in our sample as well as the average price variation during this holiday period.
Amazon made several price changes during the week but with a relatively low magnitude, since it was the lowest priced anyway through the week. The only other player with similar activity was Walmart. Target and Best Buy had significantly fewer price changes but when they did make the changes, the magnitude was much larger. Their focus was solely on a smaller, select set of products where they went all in.
In conclusion
As the years advance, the duration of holiday sales is no longer restricted to the actual holiday, but the days preceding and following them as well. With more and more people getting increasingly comfortable with online shopping (14.1% increase from 2018), buying habits are evolving too. Big retailers are cashing in on this and driving their pricing strategies to keep up with the evolution.
One of the clear cut findings from our research is that there are two primary paths they take: smaller additional markdowns over a longer period and larger additional markdowns over a shorter period. Whichever path they choose, retailers need to be on top of the game with valuable insights, that give them a competitive edge. For accurate and large scale competitive intelligence, reach out to us.
The holidays are around the corner and that much awaited holiday cheer, has now become directly proportional to the arrival of an Amazon package. According to a new report, in partnership with Bain & Company, DataWeave has observed that early in November, Amazon had the lowest price 30%-50% of the time and matched the lowest price 35%-60% of the remaining cases, based on an analysis performed on a sample of over 16,000 products across 10 websites and 5 product categories.
Aggressive pricing strategies have been Amazon’s modus operandi for a while now and it’s not about to change this season. In the build up to the Black Friday promotions this year, they even slashed their prices of the rarely discounted Apple products, such as the iPad Pro. This sets the tone for what shoppers can expect as the holiday season comes upon us.
Results of a recent survey, published as part of the Bain report, revealed that ‘value for money’ was the primary concern that influence purchasing decisions, across categories. In the same breath, the respondents went on to say that they perceive Amazon as a ‘value leader’, sans womens’ clothing and pet supplies.
Although this season might continue to see Amazon rake in the most market share, competitors are not far behind. There’s heavy investment from the likes of Walmart and others in order to negate the effects of the undercut. If these competitive responses become louder, the dent on customer perception could begin to tilt to more neutral ground.
Stay tuned as we follow this pattern during the season and release our findings over the next few weeks.
For access to the full article that was published in the Retail Holiday Newsletter by Bain & Company and powered by DataWeave, click here.
Target and Walmart offered more appealing discounts than Amazon during Prime Day 2019.
Statista estimates that e-commerce fashion accounted for approximately 20.4% of overall fashion retail sales in the United States in 2018, which amounted to about $103 billion in absolute terms. According to Internet Retailer, apparel is the largest and among the most competitive retail categories in e-commerce. Moreover, as a share of total apparel and accessories sales, online apparel sales is growing at a faster rate than US e-commerce as a whole.
Given the high-growth and competitive nature of the category, we at DataWeave were interested to find out how high the stakes got during the fifth annual Prime Day earlier this month.
Our Methodology
Since Prime Day is no longer necessarily an Amazon event (since competing websites often offer attractive discounts as well), we tracked the pricing of several leading retailers selling fashion apparel, footwear, and accessories to assess their pricing and product strategies during the sale event. Our analysis was focused on additional discounts offered during the sale to estimate the true value that the sale represented to its customers. We calculated this by comparing product prices on Prime Day versus the same prices prior to the sale.
Our sample consisted of 20 product types across women’s as well as men’s fashion categories. While we did monitor exclusive fashion retailers Macy’s, Bloomingdales, Nordstrom, and Neiman Marcus, we did not find them offering any additional discounts – an interesting insight all on its own since they’ve clearly chosen not to compete with Amazon during the two days of the Prime Day sale. We therefore restricted the rest of our study to Amazon, Target, and Walmart – the latter two of which interestingly offered immensely aggressive discounts in their apparel categories.
The Verdict
Despite owning the day at least in name, Amazon was found to offer the lowest additional discounts among the retailers studied. Target and Walmart, on the other hand, ensured that they didn’t lose out on market share this Prime Day by offering substantially high discounts of their own. While Target was the most aggressive with a steep average markdown of 26.5%, Amazon closed out the bottom at 8.4%.
Walmart and Target didn’t seem particularly focused on compensating their sharp discounts with price increases in other products – their focus seems to have been solely only on offering timely discounts during the sale. Amazon, on the other hand, marked up just about as many products as it marked down, with the markup margin being close to double that of the markdown in an effort to protect margins during the sale.
Top product types by additional discount
Target and Walmart both offered aggressive discounts across their top product categories. Walmart ended up with a marginally higher overall average additional discounts on product types like Shirts, T-shirts, and Tops.
Interestingly, though Amazon offered moderate discounts across its top categories (Lingerie, Swimwear, and Underwear), the volume of marked down products was very limited.
Additional discounts across popularity levels
We determined popularity using a combination of average review rating and number of reviews, and the resulting scores were categorized as low, moderate, and high.
When it came to discounting popular products, there were clear differences in strategy among all the three retailers. Amazon, which interestingly had close to 60% of its products in the low popularity bucket, chose to offer the highest discounts in the same category – indicating an effort to clear its stock of unpopular products. Target and Walmart, on the other hand, focused their discounts on moderate rated products.
Additional discounts across product “premiumness” levels
Premiumness was calculated as the average selling price before the sale event. This was divided into four percentile blocks, with higher percentile blocks indicating higher selling prices.
As found in the electronics and furniture categories that were analyzed previously, most of the discounting activity was focused on the lower end of the premium spectrum with a view to protect margin – despite a largely equitable distribution of discounted products across percentile ranges (with the exception of Target, which had a discounted assortment heavily dominated by its least premium products).
This indicates a clear strategy to protect margins, while still maintaining the perception of promoting attractive offers to draw traffic. Target and Walmart both offered substantial additional discounts of close to 30% on their least premium products, while at 12%, Amazon offered less than half that discount.
Additional discounts across visibility levels
Given the fairly large number of SKUs across the fashion category in general, the discounts across visibility levels understandably didn’t vary much when compared to the more pronounced fluctuations observed in the electronics and furniture categories. This is also largely because consumers tend to explore lower ranked products more so in the fashion category than in other categories.
Across product categories, we’re seeing lower-than-expected additional discounts on Amazon this Prime Day, coupled with more aggressive pricing activity by Amazon’s competitors. While this puts more pressure on Amazon, this also is a strong validation of Prime Day as a key annual sale event on the US shopper’s calendar.
Curious to know how Amazon and its competitors performed in other product categories this Prime Day? Watch this space for more!
Just as with electronics, other retailers actually offered far better discounts than Amazon during Prime Day 2019.
Online furniture sales have risen significantly since the 2000s, driven largely by a growing array of products, and even more so by the convenience of avoiding travel and crowded stores. According to Statista, online furniture and homeware sales were estimated to reach approximately $190 billion in 2018, with China and the United States accounting for over $60 billion in revenue each.
Thus, furniture has quickly become a key product category during sale events globally – and Prime Day was no different. At DataWeave, we got down to figuring out exactly how plum those deals were this year.
Our Methodology
We tracked the pricing of several leading retailers selling home and furniture products to assess their pricing and product strategies during the sale events. Our analysis was focused on additional discounts offered during the sale to estimate the true value that the sale represented to its customers. We calculated this by comparing product prices on Prime Day versus the same prices prior to the sale. Our sample consisted of the top 1,000 ranked products across 10 popular product types, including beds, dining table sets, sofas, entertainment units, and coffee tables – analyzed for five retailers (Amazon, Home Depot, Target, Walmart, and Wayfair).
The Verdict
As we found in the electronics category, there were surprising price spikes in this category too – with Target reporting an average increase as high as 14.7%, and Amazon clocking a still moderately high 9.4%. Target also reported the highest distribution of products with price markups. Home Depot indicated the lowest price increase at 4.6%.
When it came to additional discounts, Amazon fell short of expectations – at 4.7%, it offered the lowest average among its competitors. Target, on the other hand, was extremely aggressive both in terms of additional discounts and volume of discounted products.
To conclude, all the retailers observed seemed to be keeping a close watch on their margins by countering price reductions with nearly equivalent surges elsewhere in their assortment.
While there was no single product type that was found to be popular across all five retailers, it was clear that Target was again the most aggressive at offering discounts. It also had among the largest product ranges on discount.
Amazon chose to follow a very moderate route both in terms of average discount and discounted product volume.
Additional discounts across popularity levels
We determined popularity using a combination of average review rating and number of reviews, and the resulting scores were categorized as low, moderate, and high.
There doesn’t seem to have been much of a focus on low-popularity products in terms of additional discounts. Most of the attention was focused on products with moderate popularity, since there isn’t much of a need to be aggressive on price for highly popular products, and products with lower popularity aren’t really worth promoting.
The only retailer that offered a higher discount on its most popular products was Home Depot. Walmart, too, seemed reluctant to let go of the opportunity to capitalize on popularity – it chose to offer the same discount on moderately as well as highly popular products.
Interestingly, Walmart seems to have a disproportionately large share of products in its low popularity category – something it should possibly evaluate in the future in terms of brand quality, products, and service.
The percentage distribution of products mostly indicated a linear relationship, with the highest distribution usually being offered for highly popular products. The exception was Wayfair, which offered a much larger array in its moderately popular category.
Additional discounts across product “premiumness” levels
Premiumness was calculated as the average selling price before the sale event. This was divided into four percentile blocks, with higher percentile blocks indicating higher selling prices.
Most of the discounting activity seems to have occurred in the lower end of the premium spectrum, with a view to protect margin – despite a largely healthy distribution of products across percentile ranges. This indicates a clear strategy to protect margins, while also promoting attractive offers to draw traffic.
However, there are a couple of exceptions – Target was consistent throughout the “premiumness” spectrum, resulting in the highest overall discounting activity. Home Depot too was aggressive, but selectively so – it chose attractive pricing for the lower and higher ends of its assortment.
As expected, many retailers showed higher discounting activity in the higher ranks of their listing pages. As usual, though, there are a few exceptions here too. Home Depot and Wayfair indicated unusual patterns – perhaps relying on search results as opposed to organic listing page results. On the other hand, Target again indicated a consistent pattern, with mostly similar discounts across visibility levels.
Overall, across all parameters analyzed, both the Electronics and Furniture categories have been treated quite similarly in terms of pricing activity by most retailers. Is Prime Day really all about its marketing hype, or will it live up to its promise in at least one segment? Stay with us to find out as we follow through with our series of articles analyzing various product categories on this year’s Prime Day.
Our preliminary analysis reveals that Prime Day 2019 had other retailers offering better deals than Amazon in many cases.
As Prime Day extended into an additional day this year, Amazon seems to be hitting the right note with its customers, going by the revenue it’s raking in. This year, the longest Prime Day event ever witnessed a sales increase of 72% – overtaking Black Friday and Cyber Monday combined.
At DataWeave, we were curious to find out how prime these deals were, and if in fact other retailers were offering better discounts. We started with the electronics category, which remains among the most popular categories year on year.
Our Methodology
We tracked the pricing of several leading retailers selling consumer electronics to assess their pricing and product strategies during the sale event. Our analysis was focused on additional discounts offered during the sale to estimate the true value that the sale represented to its customers. We calculated this by comparing product prices on Prime Day versus the prices prior to the sale. Our sample consisted of up to the top 1,000 ranked products across 10 popular product types in consumer electronics on Amazon, Best Buy, Target, and Walmart.
The Verdict
What we found most surprising was that across retailers, some portions of the assortment underwent price increases as well. While Amazon indicated the lowest increase at 9.1%, Best Buy indicated an increase as high as 27.1%. However, Amazon reported the highest percentage of products (6.9%) that showed a price increase.
Equally surprising was that Amazon reported the lowest price reduction at 6.3% – Walmart, Target, and Best Buy in fact reduced their prices by much larger margins than Amazon did. A point to note here, however, is that Amazon did report the highest percentage of additionally discounted products – with Best Buy coming in at a close second.
This goes to show that Prime Day, for all its hype, does not in truth offer the best deals to Amazon shoppers. This, of course, is expected based on the competitors’ perspective of wanting to avoid losing market share. As a result, shoppers would be well advised to compare prices across websites to find the best deal.
Top product types by additional discount
USB flash drives were a popular product category across all four retailers analyzed, with Best Buy offering the best average additional discount at 40.7%. Other popular product types ranged from the usual personal devices such as mobile phones, tablets, and smartwatches to home appliances such as refrigerators and TVs.
Additional discounts across popularity levels
We determined popularity using a combination of average review rating and number of reviews, and the resulting scores were categorized as low, moderate, and high.
Interestingly, discounts were not found to be directly proportional to popularity. Except Walmart, all the retailers tended to offer the best discounts on products that enjoyed moderate popularity. This makes sense, since there isn’t a strong need to be aggressive on price for highly popular products in any case. On the other hand, products with lower popularity aren’t really worth promoting. Walmart, which was the exception, reported a higher discount on low- and high-popularity products than it did on moderately popular products.
The percentage distribution of products did mostly show a directly proportional relationship, with the highest distribution usually being offered for highly popular products. The exception in this case was Best Buy, which evidenced a much higher distribution in its moderately popular goods.
Additional discounts across product “premiumness” levels
Premiumness was calculated as the average selling price before the sale event. This was divided into four percentile blocks, with higher percentile blocks indicating higher selling prices.
In general, all retailers were found to have slightly higher additional discounts in the lower end of the “premiumness” spectrum. This is still a smart move, as it enables sellers to save on margin while still promoting attractive discount percentages. Interestingly, Amazon offered the lowest additional discount – a flat 5% – across all categories, despite offering more or less competitive product distributions compared to other retailers.
Additional discounts across visibility levels
Here, too, the lower end of the spectrum mostly witnessed higher additional discounts. This tactic actually offers double benefits – one, the most attractive discounts are offered in the higher realms of visibility, thus effectively enticing consumers to buy these products, and two, it helps build a low price perception (despite this not holding good as one delves deeper into the higher ranks). Again, it’s interesting to note that Amazon didn’t offer the highest discounts here either – in fact, it mostly offered the lowest additional discounts.
All in all, it seems that Prime Day isn’t all it’s hyped up to be, at least not in the Electronics segment. How about other categories? Watch this space for more insights!
Nine out of 10 leading retailers price their private label products lower than the average prices of their respective categories, reveals the latest DataWeave study, drafted in collaboration with SunTrust Robinson Humphrey The study reveals that an increasing number of retailers are viewing private label brands as a way to ensure sustained profitability.
“As the CPG space reels under intense competition, a number of retailers are doubling down on private labels to capture valuable additional margin. For instance, Kroger, Walmart, and Amazon Fresh have a higher degree of private label penetration than the other retailers we analyzed,” said Karthik Bettadapura, Co-founder & CEO at DataWeave. “Our study unveils several such key insights covering product assortment & distribution patterns, price perception, and private label dynamics, revealing a clear snapshot of the disruptive transformations sweeping across the US CPG landscape.”
Other key findings from the report, which tracked and analyzed 450,000 products across 10 leading retailers and 10 ZIP codes each, include the following:
Product assortment is emerging as a driver that’s as critical as pricing when it comes to customer retention. Target, H-E-B, and Kroger have a head start here, offering the largest product assortments among the retailers analyzed.
A sharp assortment strategy customized to local tastes and preferences is key to sustaining and enhancing customer satisfaction. Albertsons, Walmart, and Amazon Fresh lead here, revealing a higher focus on localized assortments.
“Home” and “Beauty & Personal Care” categories lead the distribution of private label products across retailers. The focus on these categories echoes a similar focus among national brands as well. These categories have the highest overall brand concentration, with around 4,000 brands each.
Online retailers in the US have enjoyed an impressive turnover during 2018’s Thanksgiving weekend sale. Over the last few weeks, DataWeave has published deep-dive reports on the performance of top US retailers in fashion and consumer electronics during this period, detailing their discounting and product strategies across several product types.
In continuation of our series of articles on the Thanksgiving weekend sale, this article focuses specifically on the top brands across all retailers analyzed.
While a lot of attention from the media and analysts during these sale events is often focused on the strategies and performance of retailers, the festive sale period is equally vital for consumer brands. Both established brands and new entrants across all categories compete aggressively to gain market share during a period that accounts for a substantial portion of annual sales turnover.
For brands, the two primary drivers of conversion specific to sale events are competitive pricing and prominent brand visibility. At DataWeave, we went about analyzing which brands came out on top across retailers and categories during the Thanksgiving weekend sale, based on these two factors.
Our Methodology
We tracked the pricing of 6 leading fashion retailers and 5 major consumer electronics retailers to study the pricing strategies of brands during the sale events. Our analysis focused on additional discounts offered during the sale period to evaluate the true value of the sale event to customers. To calculate this effect, we compared the pricing of products on Thanksgiving Day, Black Friday and Cyber Monday to the pricing of products prior to the sale commencing. We considered the Top 500 ranked products on 11 product types across Men’s and Women’s Fashion and 11 popular consumer electronics products for this analysis.
Consumer Electronics Brands
In digital cameras, Canon’s traditional role as a discount leader was on show, featuring on both Best Buy (14%) and Target (20%), the two most aggressive price discounters in consumer electronics. Nikon took Canon’s place in DSLR cameras, for Best Buy (13%), New Egg (10%) and Walmart (4%), albeit at a comparatively low additional discount point.
Razor benefited from Amazon’s strategy of promoting its lower-priced products, promoting a modest 9% additional discount but across its entire range of laptop products. The competitiveness of this category between brands is shown by Samsung’s decision to give an additional 53% discount across 36% of its product line at Best Buy.
The strategic approach brands take with different retailers was illustrated by HP’s 30% additional discount on 31% of its products at Target while over at Walmart, HP had a dire a 4% additional discount on a mere 13% of its products. A similar strategy was employed by LG with its televisions. On Amazon, its TVs had a 10% additional discount applied to 46% of its products, while at New Egg that translated to 25% and 8% respectively.
Among the fast emerging wearables category, under-pressure Chinese firm Huawei dropped an aggressive 46% additional discount on 100% of its product range at Best Buy. By comparison, the next highest in this category was Marc Jacobs at Target with 33% and 40% respectively.
Most Visible Brands Across Product Types
In our analysis, brand visibility is represented in terms of both the number of products for each brand, as well as the average rank of all its products (“lower” the rank value, higher is the visibility).
The influence an online retailer exerted on a brand’s average ranking is illustrated by Canon’s digital cameras. On Amazon, its 296 products had an average ranking of 272, while on Best Buy it was 30 and 48, 73 and 212 on New Egg and 20 and 69 on Walmart. For all these retailers, Canon was the most visible brand in digital cameras, despite such variation.
It was a similar story on laptops, with HP’s Amazon ranking of 298 based on 166 products, contrasting with a Target ranking of 14 on 18 products and Walmart ranking of 21 on 20 products.
These patterns appear to play out in TVs too, with Samsung’s Amazon average ranking of 292 based on 150 products contrasting with Walmart average ranking of 10 across 7 products.
Unsurprisingly, across our analysis of additional discounts and brand visibility, the top brands are well known and recognizable brands in each product type, with very few new entrants breaking out from the pack. This story, though, takes a turn in the following analysis on visibility growth.
Brands With Highest Growth in Visibility
To perform this analysis, we developed an index for the visibility of a brand based on the number of products available per brand as well as the average rank of those products. We then compared this score for each brand between before and during the sale period, and subsequently calculated the percentage growth.
The list of brands that showed the highest growth in visibility for each product type is an interesting mix of well established and newer brands. The usual suspects included the likes of Philips, Fitbit, Sony, Kodak, Nikon, etc. The presence of brands like Apple, Google, and Bose is surprising as they would be expected to command strong visibility even before the sale. Some of the newer brands include Rha, Westinghouse, Garmin, Lanruo, and more.
Some brands showed a dramatic increase in visibility. Examples include Bose on Walmart (698%), HTC on New Egg (657%), Galanz on Amazon (657%), and Jlab on Target (608%).
Kodak’s digital cameras (2% growth) on Best Buy took the honors for the lowest increase in visibility, just ahead of HP laptops (3%) on Walmart, Nostalgia Electrics refrigerators (4%) and Belkin Tablets (7%) both on sale at Target. These numbers indicate a relatively static assortment for the respective retailers and product types.
Fashion Brands
Moving over to the Fashion category, we observed significantly more aggressive discounting activity, as expected. Parent’s Choice T-shirts recorded the highest additional discount (80%) applied to the widest product range (Walmart 91%). Similarly, Fruit of the Loom saw Amazon promote a 78% additional discount applied across 20% of its products.
In shoes, Macy’s promoted a 60% additional discount on 50% of Kenneth Cole’s product range. In watches, Amazon featured a 57% additional discount on 50% of Kate Spade New Year branded products. Meanwhile, in sunglasses, Ray Ban in Bloomingdale’s enjoyed a 20% additional discount spread across a whopping 95% of its products, compared to just a 14% additional discount applied to a mere 10% of Ray Ban products in New Egg.
In stark contrast to what was observed in Electronics, the Fashion category saw fewer large brands dominate the discounting landscape across categories. This isn’t surprising given how the Fashion category tends to be cluttered with a plethora of brands, while the Electronics category usually consists of a leaner set of popular brands in each product type.
Most Visible Brands Across Product Types
In casual shoes, Nike’s ranking of 264 on 93 and Converse’s ranking of 239 on 89 products contrasted with Vision Street Wear’s ranking of 8 on 9 products and Time And Tru’s 15 ranking on 14 products.
Another point of contrast was Micheal Kors (Handbags) cross-retailer platform performance - its average ranking of 184 on 102 products on Macy’s while its average ranking on New Egg was 20 across 12 products. Still, it appears the brand discounted heavily in New Egg to compensate for its relatively low visibility on the website.
Ray Ban recorded a category high ranking of 209 based on 321 products on Macy’s. By comparison, Ray Ban had a ranking of 17 on 34 products at New Egg. Over at Amazon, Ray Ban managed a creditable 189 ranking on 124 products and a 163 ranking on 120 products at Bloomingdale’s.
Brands With Highest Growth in Visibility
Compared to the Electronics category, Fashion consists of certain brands that skyrocketed in their visibility. Examples include Next Level T-shirts (Amazon 2,000%), Michael Kors Watches (Walmart 1,424%), Dakota Watches (Target 751%) and Adidas sports shoes (Amazon 516%).
Bloomingdale’s delivered amazing visibility growth for key brands, with Burberry (527%), Reiss (500%), The Kooples (%00%), Tory Burch (500%), J Brand (475%), and Adidas (300%) all enjoying strong visibility growth.
At the other end of the visibility growth spectrum, the growth rates of Lucky shirts (New Egg, 11%), Micheal Kors (New Egg, 20%) Dickies jeans (Target, 22%), Tasso Elba shirts (Macy’s, 23%), and Puma Casual Shoes (Target, 25%) indicate a relatively more static assortment in their respective product types.
Depth Of Product Range And Discounting Strategy Matters
Across the three sales, DataWeave identified several different additional discounting and product assortment strategies by both the retailers and the brands.
While retailers are increasingly discounting the lower priced products to shape price perceptions among shoppers (take a bow Amazon), what are the implications for brands? Firstly, a thin product range is going to make achieving visibility more challenging. Secondly, brand strategies across online retailing platforms will need to be more clearly defined and executed. Thirdly, those brands that treated Thanksgiving, Black Friday and Cyber Monday as discrete events are going to have to rethink their approach as these lines increasingly blur with time.
If you’re interested to learn more about how DataWeave aggregates and analyzes data from online sources as massive scale, as well as how we provide competitive intelligence to retailers and consumer brands, visit our website!
Consumer electronics has always been one of the most popular product categories for consumers during the Thanksgiving weekend sale each year.
Shoppers often hold off on making expensive purchases in electronics in anticipation of great discounts during these sale events. While Cyber Monday is traditionally the key day for offers in electronics, recent trends, triggered by the growth of eCommerce, lean toward offering attractive prices across the entire sale weekend.
Studies indicate that in 2018, the average value of an online transaction hit $97. This compares with $91 in 2017 and $87 in 2016, continuing the trend of a steadily increasing transaction value over the past two years. This year, the scene was set for a massive Cyber Monday as Black Friday purchases of electronics reached $6.22 billion, up 23.6 percent from last year according to Adobe Analytics.
At DataWeave, we recently analyzed and published a blog post on the Thanksgiving weekend sale for the Fashion vertical.
As part of the same project, we scrutinized the consumer electronics vertical just as keenly across top electronics retailers in the US by monitoring prices across the weekend.
Our Methodology
We tracked the pricing of the 5 leading retailers selling consumer electronics to assess their pricing and product strategies during the sale events. Our analysis focused on additional discounts offered during the sale to evaluate the true value the sale event represented to customers. To calculate this effect, we compared the pricing of products on Thanksgiving Day, Black Friday and Cyber Monday to the pricing of products prior to the sale commencing. We considered the Top 500 ranked products on 11 popular product types in carrying out this analysis.
Key Findings
In contrast to the Fashion category, the consistency in the discounting strategy for all retailers across the three sale days in the Consumer Electronics category was striking. The only exception was Walmart, which opted somewhat curiously to roll back its discounts on Cyber Monday. All other retailers held similar additional discounts levels on a fairly similar set of products through the sale weekend.
Target and Best Buy led the electronics discount charge at 22% and 21% for 18% and 17% of their assortment, respectively.
While Amazon discounted the highest number of products at 29% of its range, it continued its recent strategy of not discounting steeply. In fact, Amazon was among the lowest in terms of additional discounts. The other end of the spectrum, Walmart provided a 28% additional discount on the first two sale days, offered only on a modest range of products (4% and 1%).
Headphones and USB Drives proved popular lead product types for discounting by all retailers. Other product types making the cut included Refrigerators (Target), Laptops (Walmart), and Wearable Technology (Newegg).
Amazon’s discounting strategy appears to be informed significantly by product visibility. The highest ranked products were far more aggressively discounted, and the discounts reduced progressively as we move to less visible products. This supports previous evidence illuminating Amazon’s strategy to develop a low price perception. We saw a similar trend emerging from Best Buy and Newegg as well.
This discounting approach is in stark contrast to the behavior we witnessed in our earlier analysis of the Fashion category, where we found little correlation between visibility and discounts. However, given the higher price points and greater price elasticity in the Electronics category, we were not surprised to see this level of strategic clarity. Interestingly, our analysis of Target’s discounting behavior showed an opposite trend as Target opted to load up discounts on its less visible products.
Walmart was excluded from this part of our study due to the very low number of common products before and during the sale that we could analyze.
Another stable trend which emerged during our analysis of the sale weekend is the consistency with which lower priced products are offered at higher additional discounts relative to the more premium, higher priced products in the retailers’ product type. This trend largely held across retailers. Customer perceptions of low prices can be built by heavily discounting products at the lower end of the premium spectrum, while retailers can harvest their critical margin on their higher value goods.
Diving Deeper Into Amazon
Amazon announced a few days ago that it had its biggest shopping day in the company’s history on Cyber Monday. In its announcement, the company also stated the five shopping days starting with Thanksgiving and continuing through to Cyber Monday shattered records as US consumers bought millions of more products over the five-day sales compared with the same sales period last year.
When the product popularity was evaluated and compared with additional discounts, we see higher discounts for better-reviewed products on Thanksgiving and Black Friday. Cyber Monday was an exception where discounts were distributed more smoothly across the three popularity bands.
As with what we witnessed in the Fashion category, we detected higher additional discounts in Amazon’s Electronics private label brands (17%) relative to the average discount for other brands (7%).
Profitability is back in the spotlight
Electronics continued to be a key focus eCommerce retailers during their pivotal sales events in 2018. We are seeing signs of a shift to eCommerce and an accelerating emergence of a “Black November” and a “Cyber Post-Thanksgiving Weekend” impacting on sales results for the beginning of the holiday season.
This year, there was a more concerted and strategic approach by retailers to maximize margin in the high-value end of the Electronics Category while still discounting the more popular and lower priced products. As expected, both Target and Best Buy featured prominently with their heavy discounting, while both Amazon and Newegg appeared to be executing a more nuanced discounting strategy. This rather reserved approach to the sale and careful focus on profitability is backed up by recent reports of Amazon’s shift in approach to housing low margin products.
As was the case with the Fashion category, we saw the importance of Cyber Monday for Electronics sales being eroded and spread across the entire weekend, on the backdrop of a larger trend of attractive offers encompassing much of November and December.
If you would like to know more about how DataWeave aggregates data from online sources to deliver actionable insights to retailers and consumer brands, check out our website!
The biggest holiday sale event of the western retail calendar — the Thanksgiving weekend sale, which includes Thanksgiving Day, Black Friday, and Cyber Monday — came and went a few weeks ago and made a huge splash along the way. While the sale event, especially Black Friday, is traditionally an offline sale event, modern online retailers too step up to offer products at attractive prices through this period.
Online retail sales numbers grew at an impressive clip based on stats reported by Adobe Analytics. Thanksgiving Day sale itself generated $3.7 billion in sales, up 28 percent from a year ago. Black Friday delivered a record $6.22 billion in online sales — a substantial leap of 23.6 percent year on year. Cyber Monday sales online generated a new record of $7.9 billion, up nearly 18 percent from last year.
Spending on fashion specifically was up 5.4 percent over the 2018 Black Friday weekend, the best growth seen since 2011, according to consulting firm Customer Growth Partners. Apparel retailers now book nearly a quarter of their annual sales during these holiday sales — a measure of just how important these annual sales have become to the online retailer’s commercial performance.
As a provider of Competitive Intelligence as a Service to retailers and consumer brands, DataWeave consistently monitors and captures pricing and assortment information from leading retailer websites during sale events to study their product and pricing strategies — and we’ve done the same for this year’s Thanksgiving weekend sale as well.
Our Methodology
We tracked the pricing of 6 leading fashion retailers to study their pricing and product strategies during the sale events. Our analysis focused on additional discounts offered during the sale to evaluate the true value of the sale event to customers. To calculate this effect, we compared the pricing of products on Thanksgiving Day, Black Friday and Cyber Monday to the pricing of products prior to the sale commencing. We considered the Top 500 ranked products on 15 product types across Men’s and Women’s Fashion for this analysis.
Key Findings in Men’s Fashion
Macy’s and Bloomingdale’s featured prominently among the top discounting retailers. This is unsurprising, given their focus on Fashion. Macy’s, in particular, additionally discounted just over half its fashion assortment over the three days. This was an order of magnitude greater than its nearest competitor Amazon at 29 percent.
Target and Walmart too discounted aggressively on Thanksgiving and Black Friday. Target exceeded Macy’s by 2 percentage points. However, Target and Walmart rolled back their discounts on Cyber Monday effectively halving them.
Walmart’s discount strategy displayed significant variation across the 3 days of sale. On Black Friday, Walmart led the retailing pack with its 46 percent discount only to roll back to 15% on Cyber Monday. The fluctuations in these discounts reflect significant variation and churn in Walmart’s Top 500 ranked products across the three days of sales.
As we have seen in previous sales, Amazon was a model of consistency in its discount strategy across the three days, maintaining a healthy 15% — 16% on roughly a third of its assortment. Strikingly, Newegg elected not to compete too aggressively in Fashion this year, adopting high single digit discounts on a similar percentage of its products.
Across all six retailers, Shirts, Jeans, and T-shirts proved to be the most popular product types in terms of additional discounts although accessories such as sunglasses (Newegg) and watches (Macy’s) broke up apparel’s dominance.
Did additional discounts vary by price range?
We also studied the variation of discounts across ranges of product “premiumness”. We generated a percentile scale based on price ranges of products from before the sale, and studied the additional discounts offered for products in these price range buckets during the sale. A percentile score or 1 is the cheapest product and 100 is the most expensive product. All of these metrics were calculated first at a product type level and then aggregated at an overall level for each retailer.
Amazon and Target display a clear strategy to additionally discount their more affordable range of products – those in the 1–20 cluster.
Bloomingdale’s showed a less structured strategic approach. Its additional discounts were largely spread evenly across levels. Its product churn among the Top 500 items during the sale focused on its more expensive products as indicated by its score of 0 for the 81–100 percentile bracket.
Macy’s opted to discount even more evenly across the board than Bloomingdale’s. It’s likely Macy’s relied on a different lever to drive discounts strategically. Walmart’s pricing approach was markedly uneven and all over the board from a strategic perspective.
Key Findings in Women’s Fashion
One of the most interesting patterns to emerge from these sale events was the marked difference in discounting strategy adopted for Women’s Fashion compared to Men’s Fashion. Both Amazon and Macy’s discounted their Women’s Fashion line up far less aggressively than their Men’s Fashion products. Their discounts also applied to a smaller set of products.
Bloomingdale’s Women’s Fashion discounting was similarly marginally less aggressive than its approach to its Men’s Fashion. Only Target’s pricing remained consistent across its Men’s and Women’s Fashion products. However, Newegg’s strategy of not engaging too aggressively in Men’s Fashion this year carried over to its treatment of Women’s Fashion.
The top product types additionally discounted were also not unexpectedly different between the Men’s and Women’s Fashion products. Skirts, Shoes, and Tops emerged as the favorite product types to discount, although no two retailers had the same discounting emphasis.
As with Women’s Fashion, Amazon and Target discounted their less expensive products more consistently. However, in Women’s Fashion, they were joined by Walmart and to a lesser degree, Newegg.
This showed evidence of a strategy to retail the less expensive products at more attractive price points to generate the price perception of being low-priced. Meanwhile, they continued to harvest comparatively more margin through their more expensive products. This was a more nuanced approach to margin management than what we saw in Men’s Fashion.
Does product visibility correlate with discounts?
One working hypothesis is that products discounted heavily tend to have higher visibility to drive the perception of lower price. However, the results of our analysis appear counter-intuitive.
Amazon’s additional discounts in Men’s Fashion appear relatively uniform across all product cohorts. In fact, Amazon’s peaked additional discounts with the 200–400 cohort.
Similar trends surfaced with other retailers. Newegg additionally discounted its longer tail products, while Walmart additionally discounted its Top 50 products at only 16% compared to an average of around 23% for other cohorts in its Top 500.
We extracted data on Amazon’s reviews and ratings to investigate its discounting strategy across ranges of product popularity — a measure that’s defined using a combination of average review rating and number of reviews. We compiled a measure of all products that were rated as High, Medium, and Low cohorts and evaluated Amazon’s discounting strategy in each cohort.
In Men’s Fashion, Amazon aggressively discounted its Medium and Low rated products on Thanksgiving, only to switch its strategy the next day on Black Friday. This tactical switch was presumably intended to showcase Amazon’s well-reviewed products at attractive prices on Black Friday — a larger sale event.
By Cyber Monday, Amazon’s Medium reviewed products were back enjoying more aggressive discount levels, albeit the discount variance across all three cohorts was minor.
Amazon’s discounting strategy for its Men’s Fashion products was in stark contrast to its strategy in Women’s Fashion. Here, Amazon additionally discounted its High and Medium reviewed products on Thanksgiving. While there was no specific discernible pattern on Black Friday, Amazon’s discounting was most consistent across its three popularity cohorts on Cyber Monday.
We also looked at Amazon’s discounting activity across its private label products relative to other brands. Unsurprisingly, Amazon discounted its private label fashion products at an aggressive 30%, while the other brands benefited from, on average across all days and all categories, an additional 15% discount.
Online drives shifting tides in holiday sale events
While traditionally the holiday shopping season sees a peak around Black Friday and Christmas, retailers are increasingly seeing the demand spread across the entirety of the sale season of November and December. As a result, retailers need to stay on their toes to drive increased sales and gain market share over an extended period of time.
Certainly, in 2018, we witnessed a more focused approach to mine margins in Women’s Fashion while still discounting aggressively. As expected, both Macy’s and Bloomingdale’s featured prominently in the discounting stakes while both Amazon and Target appeared to implement a more nuanced approach to juggling a reputation for low prices and driving increased margin.
If you’re curious about how DataWeave aggregates data from eCommerce data at massive to deliver actionable insights to retailers and consumer brands, check us out on our website!
An average of $11.7 million per second was the rate at which Alibaba clocked $1 billion in sales during the first 85 seconds of Singles’ Day. As Alibaba’s annual sale event continues to grow in scale, referring to it as a global retail phenomenon is an understatement. Alibaba closed the day having shipped 1.04 billion express packages based on sales of merchandize worth 213.5 billion yuan ($30.67 billion).
This performance shredded any lingering concerns analysts may have harbored about the prospects of this year’s sale, given the international backdrop of the ongoing trade skirmish between the US and China.
Along with attractive discounts across a range of product categories, Singles’ Day also promised an integrated experience fusing entertainment, digital and shopping, in stark contrast to other large global sale events like Black Friday, which focus predominantly on discounts.
At DataWeave, we set out to investigate if all the hype resulted in actual price benefits to the shoppers and how the various categories and brands performed in terms of sales during the event. To do this, we leveraged our proprietary data aggregation and analysis platform to capture a range of diverse data points on Tmall Global, covering unit sales (reported by the website) and pricing associated with Tmall Global’s major categories over the Singles’ Day period.
Our Methodology
We captured 5 separate snapshots of data from Tmall.com during the period between October 25 and November 14, encompassing over 15,000 unique products each time, across 15 product categories.
To calculate the average discount rate, we considered the percentage difference between the maximum retail price and the available price of each product. We also looked at the additional discount rate, for which we compared the available price during Singles’ Day to the available price from before the sale. This metric reflects the truest value to the shopper during Singles’ Day in terms of price.
Our AI-powered technology platform is also capable of capturing prices embedded in an image. For example, the offer price of ¥4198 was extracted accurately from the accompanying image by our algorithms and attributed as the available price while ¥100 from the same image was ignored.
This technology was employed across hundreds of products using DataWeave’s proprietary Computer Vision technology.
Domestic Appliances and Digital/Computer Categories Powered Turnover
The Domestic Appliances and Digital/Computer categories dominated the Singles Day Sale in terms of absolute sales turnover. This isn’t surprising, since the average order value for these categories are typically much higher compared to the other categories analyzed.
What clearly stands out in the above infographic is that the two largest categories in terms of sales turnover had average additional discounts of only 2 per cent and 0 per cent — a rather surprising insight. In general, with the exceptions of Women’s skincare, Men’s skincare, and Women’s bags (11 per cent, 10 per cent, and 9 per cent respectively), all other categories saw low additional discounts during Singles’ Day.
However, the absolute discounts across the board were consistently high, with only Luggage (6 per cent), Digital/Computer (9 per cent) and Women’s wear (12 per cent) staying significantly below the 20 per cent mark. In fact, eight categories enjoyed absolute discounts greater than 30 per cent.
Among common categories between Men and Women, the Men clocked more sales in Men’s wear, shoes, and bags. Only skincare proved to be an exception, where Women’s skincare generated twice the turnover of their Men’s equivalent.
The Infants category was another intriguing sector to emerge during the sale. Both Diapers (38 per cent) and Infant’s Formula (25 per cent) were substantially discounted, despite only receiving low additional discounts of 2 per cent and 0 per cent respectively – indicating aggressive pricing strategies in this category even during non-sale time periods.
The biggest takeaway from our analysis is the lack of any correlation between sales turnover and additional discounts, or even the absolute discounts.
International Brands Make Gains
International brands continue to penetrate the Chinese market showing up amongst the Top 5 brands of 13 of the 16 categories on sale.
In the Diaper category, Pampers delivered nearly twice the sales turnover of its next biggest competitor. As expected, Apple and Huawei battled it out for honors in the Digital/Computer category although Xiaomi enjoyed pleasing results, nearly matching Huawei’s sales to go with its sales leadership of the Domestic Appliances category. Local brands, though, swept the Domestic Appliances, Furniture and Women’s Wear categories.
The challenge posed by Chinese brands was illustrated by Nike’s spot in the second place in the highly competitive Men’s Shoes category after Anta.
International brands topped only five of the 16 categories and Top 3 positions in ten categories. Still, there’s a growing presence of international brands in China’s eCommerce.
Gillette won handsomely over its competition in the Personal Care category while Skechers enjoyed a similar result in Women’s Shoes, racking up nearly twice the retail sales of its nearest competitor. Another category dominated by international brands was the Women’s Cosmetics category where international brands accounted for 4 of the Top 5 brands.
Similarly, Samsonite’s acquisition of American Tourister gave it two top 5 brands in the Luggage category. Other global brands to make the cut during the Singles’ Day sale included L’Oréal, Canada’s Hershel, Playboy, South Korea’s Innisfree and Japan’s Uniqlo.
It’s Not All About Price On Singles’ Day
The dramatic rise in shopping during Singles’ Day is not driven solely by price reductions. Alibaba’s commitment to its “New Retail” strategic model has led the Chinese giant to channel its impressive resources to focus on bringing together the online elements of its business with the more traditional offline aspects of its retail distribution. This is combined with entertainment to create a larger story based around the shopper’s overall “experience” rather than just driving “attractive prices” as a short-term retail hook.
Alibaba is betting big on erasing the line between online and offline and its futuristic vision of structuring retail around the way people actually want to shop. Based on the consistently impressive results of Singles’ Day year after year, “New Retail” has a promising future.
If you wish to know more about how DataWeave aggregates data from online sources to provide actionable insights to retailers and consumer brands, check out our website!
Recently, Karthik Bettadapura, Co-founder & CEO at DataWeave, was interviewed by Vishal Krishna, Business Editor at YourStory, in the Bay Area, California. They discussed DataWeave’s focus on the US market, challenges that retailers face today, DataWeave’s technology platform and hiring practices, and more.
The following is a transcript of the interview.
(The transcript has been edited for clarity and brevity)
Vishal Krishna (VK): You left India to come and conquer America, why is that?
Karthik Bettadapura (KB) : Just a bit of history — we started in 2011 and product development and research was based in Bangalore, and still is. At the end of the first 5 years, we realized that we built great technology, but we were not able to scale beyond a certain point [in India]. If we had to build a growing business, we had to look at other markets as well.
VK:Quickly, can you tell me what DataWeave does?
KB: We provide Competitive Intelligence to retailers and customer brands. We work with some of the largest brands and retailers out there and we provide them with analyses to compete profitably.
VK:You said you had marque clients in India, yet you didn’t want to stay there because you wouldn’t have scaled beyond a particular point. Why is that?
KB :The ticket size in India is still on the lower side. If you must build a sustainable business, you need access to a much larger customer base and we found that in the US.
VK:Let’s start from the basics. What are a few things that a startup should decide to do when coming to America?
KB: A few things:
A good understanding of the market
Learn fast about the market
Build a team here, or a have a team here already doing some work initially
Consider how your team back in India will go about doing things in your absence
The last one is about your own personal journey. I was so used to walking into an office and interacting with people. You come here, and you are all alone!
VK:It’s a lonely journey. Doors don’t open all that easily and you’ve got to hustle. Why?
KB: For people here, you are an unknown entity. Why should they be trusting someone who does not have enough customers here or has not raised money here? We had two US-based customers when we came in. It’s an uphill task to ensure that customers trust you.
VK:Who was the first customer you personally met here and why was that meeting so important?
KB: The first customer I met here was a large, big box retailer, and the meeting was primarily focused around why they should trust us — how can they know that we would survive and serve them, as well as how we are better than some of the other guys out there.
VK: Can you tell us what DataWeave does for US retailers?
KB: For retailers, we provide competitive intelligence, primarily around pricing optimization and assortment analytics. In the US, a lot of retailers are shutting shop and filing for bankruptcy.
VK: Yeah, we saw Sears go through something like that.
KB: The reasons fall broadly into 3 categories:
They failed to compete profitably with a lot of these new age businesses.
The new age retailers offer superior customer experience. They have figured out a better assortment/product strategy.
The third one is ‘Price’ — price is such an important feature.
What we do is help these retailers optimize their strategies around pricing, assortment and promotions, eventually enabling them to compete profitably.
VK:Typically, customers pay you on the outcome, pricing, license or subscription?
KB: It’s a subscription-based model. There is a one-time setup fee and an ongoing subscription fee.
VK:So you plug into their data management system?
KB: Yes, but we can also have our product sit independently. Sitting out of their internal systems is a benefit for us as we don’t have to get into the entire loop of integrations into their internal systems right from Day 1. We prove our product works and then we integrate with their systems.
VK:How do you integrate? Is the CIO your target?
KB: No, we don’t sell to the CIO world. We sell to analytics, pricing, and merchandising teams.
VK:Can pricing alone give retailers a competitive edge?
KB: Yes, pricing is a big lever that retailers use. For example, last holiday season’s sale, Amazon and Walmart made 120 million price changes in just 2–3 days.
VK: So they change the prices so dynamically to compete with each other. Is this price war coming to India?
KB: It is happening in India already.
VK:How much data can DataWeave’s infrastructure ingest?
KB: We are a global platform — we have customers across the globe, not just the US or India. So, on a daily basis, we process data on around 120 million products.
VK: Talk a little bit on R&D quickly. Do you have your marketing team in the US?
KB: We have marketing teams in the US and India.
VK: And the engineering team?
KB: The engineering team is in Bangalore.
VK:For people who want to work in your company, what kind of talent are you looking for?
KB: We look at 4 broad talent areas:
One is in the world of data acquisition, which addresses issues like how data can be aggregated from thousands of websites and millions of pages on an ongoing basis, and how this data can be stored.
The second area is on what kind of insights can be generated using this data. This could be done using text analytics, image analytics, and other technologies. This includes process optimization, in terms of building efficient and scalable systems.
The third area is on how well the data can be represented if we have a customer who wants 60–70 million data points to be consumed on a weekly basis.
And the last area is on data modeling — what kind of insights can we eventually give to the customer? And, when I say insights, I mean specific actions.
VK:You want people who can handle massive scale and for that they should be good at linear regression.
KB: We value people who write good code. We primarily work in Python, and we use a lot of optimization techniques in the middle of the stack to help us scale.
VK: Would you do something for supermarkets?
KB: Absolutely. The largest offline supermarket in India is our customer.
VK: So what can you do for supermarkets?
KB: Offline retailers across the world are facing something that’s called showrooming. This is when a shopper walks up to a store, looks at and feels a product, then searches online to see it’s available at a better price. So we have retailers who are wary of this phenomenon. We also have retailers who are wary of diminishing customer loyalty. So they have to constantly ensure that they are priced better in the market and are not losing customers because of [online] pricing.
VK:How powerful are your algorithms?
KB: There is a dedicated team that works on our algorithms. These fall into several buckets. One is pure data scale algorithms — how do you build systems which ensure that you are able to efficiently query them in real time and get the desired output. The second one is — how do you keep improving your machine learning algorithms. For example, computer vision algorithms, text analytics algorithm, etc. The third — how do you keep experimenting effectively.
VK: What role can an MBA degree holder play in DataWeave?
KB: We have people who hold MBA degrees and are working in customer success, delivery management, marketing, and sales.
VK:Do you spend time in training?
KB: You do have some lead time if you are a fresher, but if you are a lateral hire, its expected that you keep the ball rolling. They should be able to learn and learn fast — learning is more important than knowing. So, we give a lot of importance to people who can learn and pick up things quickly – about our product, handling customer objections, etc.
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Watch the whole video here or check out DataWeave’s website to know more about how we use data engineering and artificial intelligence to enable retailers and brands to compete profitably in the age of eCommerce.
As with many other product categories, Amazon has made a significant incursion in Apparel — a key battleground category in retail today. Recently, DataWeave once more collaborated with Coresight Research, a retail-focused research firm to publish an in-depth report revealing insights on Amazon’s approach to its US fashion offerings.
Since our initial collaborative report in February this year, we have witnessed some seismic shifts in the category at both the brand and the product-type level.
Research Methodology
We aggregated our analytical data on more than 1 million women’s and men’s clothing products listed on Amazon.com in two stages:
Firstly, we identified all brands included in the Top 500 featured product listings for each product subcategory in both the Women’s Clothing and Men’s Clothing sections featured on Amazon Fashion (e.g., the top 500 product listings for women’s tops and tees, the top 500 product listings for men’s activewear, etc.). We believe these Top 500 products reflect around 95 percent of all Amazon.com’s clothing sales. This represents 2,782 unique brands.
We then aggregated the data on all product listings within the Women’s Clothing and Men’s Clothing sections for each of those 2,782 brands. This generated a total of 1.12 million individually listed products. This expansive list forms the basis for our highlights of the report.
Third-Party Seller Listings Are Rising Sharply
We identified a total of 1.12 million products across men’s and women’s clothing — a significant increase of 27.3 percent in the seven months between February and September 2018. The drivers of this sharp spike are third-party seller listings. In contrast, the report indicates only a 2.2 percent rise in first-party listings over the same period, compared to a 30.5 percent jump in third-party listings.
In addition, Amazon has listed just 11.1 percent of all clothing products for sale, with third-party sellers offering the remaining 88.9 percent — an indication of the strength of Amazon’s open marketplace platform.
A Major Brand Shift On Amazon Fashion Is Underway
In just over six short months, major brand shifts on Amazon Fashion have taken place. The number of Nike listings has plummeted by 46 percent, driven by a slump in third-party listings following Amazon’s new partnership with Nike — a story recently covered by Quartz. Limited growth in Nike clothing first-party listings failed to compensate for this decline.
Gildan’s spike in total product listings appears to be fueled by increased first-party listings off a low base. Calvin Klein’s 2017 agreement to supply Amazon with products appears to be driving the Calvin Klein brand’s double-digit uptick in first-party listings on Amazon Fashion.
Aéropostale’s decline appears to be entirely driven by a drop in its third-party listings. The brand itself is not listed as a seller on Amazon.com.
Amazon Is Rebalancing Its Apparel Portfolio and Switching Its Focus from Sportswear To Suits
As its Fashion footprint rapidly matures, Amazon now appears to be rebalancing its portfolio with strong growth being shown in listings for formal categories such as suits and away from sportswear. We recorded a 98.6 percent increase in listings of women’s suits and blazers complemented by a 52.2 percent rise in men’s suit and sports coat listings between February and September 2018.
Generic “Non-Brands” Are Surging On Top 25 Brands List
Over the past six months, low-price generic brands have made major inroads into Amazon’s listings. Four unknown “brands” captured the top positions on the list of brands offered on Amazon Fashion. The WSPLYSPJY, Cruiize and Comfy brands appear to be shipped directly to customers from China.
Source: Coresight/DataWeave (Amazon Fashion: Top 25 Brands’ Number of Listings, February 2018 vs. September 2018)
Source: Coresight/DataWeave (Amazon Fashion: Top 25 Brands’ Number of Listings, February 2018 vs. September 2018)
WSPLYSPJY alone accounts for fully 8.6 percent of Amazon men’s and women’s clothing listings. Cruiize accounts for a further 3.2 percent of listings while Comfy chips in another 3.1 percent.
Amazon Appears To be Executing A Strategic Pivot
Amazon’s fashion offering is fast maturing. We saw substantial growth in the number of listings for more formal categories. The realignment in third-party listings by Nike together with increased first-party listings for Calvin Klein and Gildan appear to be driven by alliances with Amazon.
Simultaneously, ultralow-price generic clothing items delivered on order from China have inundated the “Most-Listed Products” rankings. Third parties now represent nearly 90 percent of Amazon Fashion’s offering.
While Amazon Fashion shoppers enjoy a wider choice than they did even six months ago, we believe a stronger emphasis on first-party listings would grow the products eligible for Prime delivery. This tactic could strengthen Amazon Fashion’s long-term appeal as a shopping destination.
If you’re interested in DataWeave’s technology, and how we aggregate data from online sources to provide unique and comprehensive insights on eCommerce products and pricing, check us out on our website!
It’s festival season in India again and shoppers took advantage of aggressive cutthroat competition between Indian online retailers to drive sales to unprecedented highs.
All the major Indian eCommerce websites including, Amazon, Flipkart, Myntra, and Shopclues opted to go head to head by holding their first sale event this season over 4 to 5 days starting on the 10th of October. Still, as industry reports indicate, one retailer came out on top during this event — an insight supported by our analysis as well.
A New Battleground
The highlight this year was seeing how the announcement of global retail colossus Walmart’s acquisition of Flipkart would impact the sale events. The acquisition was the most influential development in India’s eCommerce sector, and it has transported a decades-long U.S. rivalry between Amazon and Walmart to Indian soil. As a result, this year’s sale event held out the promise of more attractive pricing and vast product selection for India’s consumers than ever before.
Industry analysts estimate that the sale generated a cumulative Rs 15,000 crore in sales over the spread of the five sale days, a whopping outcome. In 2018, this translated into around a 64 per cent year-on-year growth outcome compared to the USD 1.4 billion (around Rs 10,325 crore) generated by the 2017 sales.
The DataWeave Analysis
At DataWeave, we analyzed the performance of each of the major eCommerce platforms including Amazon, Flipkart, Myntra, Paytm, and Shopclues. For each eCommerce website, we aggregated data on the Top 500 ranked products for over 40 product types spread across 6 product categories (Electronics, Men’s & Women’s Fashion, Furniture, Haircare, Skincare).
We focused our analysis on only the additional discounts offered during the sale and compared them to prices prior to the sale, to reflect the true value of the sale to India’s shoppers.
The battle of the discounts was led primarily by Flipkart and Amazon. Flipkart’s average additional discounts by category actually exceeded Amazon’s in three out of six categories, and it discounted more products that Amazon across all categories.
Clearly, the focus for all e-tailers was skewed towards the main battlegrounds of Electronics and Fashion, compared to mainstream FMCG categories such as Hair and Skin Care. However, this is not surprising given FMCG functions on rather skinny margins.
Across retailers, the Men’s and Women’s Fashion categories were the most aggressively discounted, attracting both the highest additional discounts and the highest percentage of products with additional discounts.
The Furniture category too was an interesting battleground between Amazon and Flipkart, attracting attractive discounts on a wide range of products, particularly in Flipkart’s case.
Prospective shoppers in search of relatively more expensive clothing products on discount during the sale would have established Myntra as their ideal destination, as it carried more premium products on discount during the sale, relative to all its competitors. For shoppers in search of an electronics bargain though, they would have done well to opt for Flipkart.
Shoppers may have found some interesting deals on Paytm Mall too, especially in Men’s Fashion, while Shopclues largely held itself back from any dramatic price reductions.
While Myntra capitalized on its niche though aggressive discounting in the Fashion category, most of the discounting action revolved unsurprisingly around Amazon and Flipkart. To drill down for a more complete understanding of just how the Amazon and Flipkart discounted their products, we conducted a more detailed follow-on analysis.
We normalized additional discounts and popularity using a scale of 1 to 10 and plotted each product on a chart to analyze its distribution characteristics. Popularity was calculated as a combination of the average review rating and the number of reviews posted. Products with a popularity score of zero, as well as zero additional discounts were excluded from this analysis.
The most obvious insight yield through this analysis is how Flipkart elected to distribute its additional discounts across a larger range of discount percentages. By contrast, Amazon went all in on the more limited range of products it decided to provide additional discounts on. This is a strategy we have seen Amazon adopt previously.
One other intriguing insight is Flipkart’s decision to go for a much higher distribution of products falling below a popularity score of 0.5 compared to Amazon. Amazon’s strategy resulted in more of its discounted products having a higher popularity score, relative to Flipkart, albeit only by a comparatively minor amount. However, a shopper’s chances of buying a popular, positively reviewed product at a lower price were higher on Amazon than Flipkart during this sale.
Achieving a Consistent Competitive Edge
Flipkart claims to have recorded a 70 per cent plus share of entire Indian e-commerce market in the 4 day-BBD’18 sales. Flipkart further claimed to have cornered an 85 per cent share in the online Fashion category together with a 75 per cent share in the Electrical category’s large appliances during the sale. This includes a contribution by Flipkart’s subsidiary Myntra.
As these numbers reflect, Amazon still has some way to go to entrench itself in the Fashion category of the Indian market. However, Amazon appears content to continue its surgical discounting philosophy.
Overall, this year witnessed an impressive participation by Tier II and Tier III Indian city consumers — a sign of things to come in Indian online retail.
With increasing competitive pressure, retailers simply cannot adopt discounting and product selection strategies in isolation and be successful. Having access to up to date insights on competitors’ products dynamically during the day is emerging as key to ensuring they’re able to sustain their lowest priced strategy for appropriate products. These insights are also proving critical in identifying gaps in their product assortment, which can hamper customer conversion and retention.
During sale events, modern retailers need to rely on highly granular competitive insights on an hourly basis (or even more frequently) to inform their pricing and product strategies to ensure they consistently maintain a competitive edge for the consumer’s wallet. And while access to reliable competitive intelligence is critical, true value can only be derived when it gets integrated with a retailer’s core business and decision-making processes, such as assortment management, promotions planning, pricing strategies, etc.
DataWeave’s Competitive Intelligence as a Service helps global retailers do just this by providing timely, accurate, and actionable competitive pricing and product insights, at massive scale. Check out our website to find out more!
Natt Fry, a renowned thought leader in the world of retail and analytics, published recently an article expounding the value and potential of learning models influencing business decision-making across industries over the next few years.
He quotes a Wall Street Journal article (paywall) published by Steven A. Cohen and Matthew W. Granade who claim that, “while software ate the world the past 7 years, learning models will ‘eat the world’ in the next 7 years.”
The article defines a learning model as a “decision framework in which the logic is derived by algorithm from data. Once created, a model can learn from its successes and failures with speed and sophistication that humans usually cannot match.”
Narrowing this down to the world of retail, Natt states, “if we believe that learning models are the future, then retailers will need to rapidly transform from human-learning models to automated-learning models.”
This, of course, comes with several challenges, one of which is the scarcity of easily consumable data for supervised learning algorithms to get trained on. This scarcity often results in a garbage-in-garbage-out situation and limits the ability of AI systems to improve in accuracy over time, or to generate meaningful output on a consistent basis.
Enabling Retailers Become More Model-Driven
As a provider of Competitive Intelligence as a Service to retailers and consumer brands, DataWeave uses highly trained AI models to harness and analyze massive volumes of Web data consistently.
Far too often, we’ve seen traditional retailers rely disproportionately on internal data (such as POS data, inventory data, traffic data, etc.) to inform their decision-making process. This isn’t a surprise, as internal data is readily accessible and likely to be well structured.
However, if retailers can harness external data at scale (from the Web — the largest and richest source of information, ever), and use it to generate model-driven insights, they can achieve a uniquely holistic perspective to business decision-making. Also, due simply to the sheer vastness of Web data, it serves as a never-ending source of training data for existing models.
DataWeave’s AI-based model to leverage Web data
Web data is typically massive, noisy, unstructured, and constantly changing. Therefore, at DataWeave, we’ve designed a proprietary data aggregation platform that is capable of capturing millions of data points from complex Web and mobile app environments each day.
We then apply AI/ML techniques to process the data into a form that can be easily interpreted and acted on. The human-in-the-loop is an additional layer to this stack which ensures a minimum threshold of output accuracy. Simultaneously, this approach feeds information on human-driven decisions back to the algorithm, thereby rendering it more and more accurate with time.
Businesses derive the greatest value when external model-based competitive and market insights are blended with internal data and systems to generate optimized recommendations. For example, our retail customers combine competitor pricing insights provided by our platform with their internal sales and inventory data to develop algorithmic price optimization systems that maximize revenue and margin for millions of products.
This way, DataWeave enables retailers and consumer brands to utilize a unique model-based decision framework, something that will soon be fundamental (if not already) to business decision-making across industry verticals and global regions.
As AI-based technologies become more pervasive in retail, it’s only a matter of time before they’re considered merely table stakes. As summarized by Natt, “going forward, retailers will be valued on the completeness of the data they create and have access to.”
If you would like to learn more about how we use AI to empower retailers and consumer brands to compete profitably, check out our website!
Read Natt’s article in full below:
Steven A. Cohen and Matthew W. Granade published a very interesting article in the Wall Street Journal on August 19, 2018 — https://www.wsj.com/articles/models-will-run-the-world-1534716720
Their premise is that while software ate the world (Mark Andreessen essay in 2011, “Why Software is Eating the World”) the past 7 years, learning models will “eat the world” in the next 7 years.
A learning model is a decision framework in which the logic is derived by algorithm from data. Once created, a model can learn from its successes and failures with speed and sophistication that humans usually cannot match.
The authors believe a new, more powerful, business opportunity has evolved from software. It is where companies structure their business processes to put continuously learning models at their center.
Amazon, Alibaba, and Tencent are great examples of companies that widely use learning models to outperform their competitors.
The implications of a model-driven world are significant for retailers.
Incumbents can have an advantage in a model-driven world as they already have troves of data.
Going forward retailers will be valued on the completeness of the data they create and have access to.
Retailers currently rely on the experience and expertise of their people to make good decisions (what to buy, how much to buy, where to put it, etc.).
If we believe that learning models are the future then retailers will need to rapidly transform from human-learning models to automated-learning models, creating two significant challenges.
First, retailers have difficulty in finding and retaining top learning-model talent (data scientists).
Second, migrating from human-based learning models to machine-based learning models will create significant cultural and change management issues.
Overcoming these issues is possible, just as many retailers have overcome the issues presented by the digital age. The difference is, that while the digital age has developed over a 20 year period, the learning-model age will develop over the next 7 years. The effort and pace of change will need to be much greater.
Another year, another round of media frenzy, and another set of records broken.
In only three years, Amazon’s Prime Day has evolved into one of the landmark sale events of the shopper’s calendar. Reports indicate that this year’s sale made a major splash, raking in over $4.2 billion in sales — a 33% increase compared to last year. Also, the retail behemoth shipped over 100 million products during the 36-hour sale. Amazon stated that they “welcomed more new Prime members on July 16 than on any other previous day in Prime history.”
The much talked about website outage added some spice and drama to the proceedings during the first hour. However, this was fixed quickly.
This year is also the first Prime Day with Whole Foods, Amazon’s most expensive acquisition, giving US shoppers unprecedented incentives to shop at the physical stores of the grocery retailer.
However, Prime Day is not just about the US, but a truly global event. In India, as part of its promotions for Prime Day, Amazon leveraged VR to have people experience the products in their true form factor at select malls.
At DataWeave, our proprietary data aggregation and analysis platform enabled us to keep an eye on the pricing and discounts of products during the sale. We tracked Amazon.com, Amazon.co.uk, and Amazon.in before (14th July) and during the sale (16th July) and monitored several product types in Electronics, Men’s Fashion, Women’s Fashion and Furniture categories. We captured information on the price, brand, rank on the category page, whether Prime was offered or not, etc. and analyzed the top 200 ranks in each product type listing page. To best indicate the additional value to shoppers during the sale, we focused our analysis only on additional discounts on products between the 14th and 16th of July.
Scrutinizing the data yielded some rather interesting insights:
Amazon UK was more aggressive with its discounts than the US and India across most categories, with Furniture being the only exception (highest discounts in the US).
In the US, Women’s Fashion observed the steepest discounts (12%), though there were discounts available on a larger number of Men’s Fashion products (5% additional discount on 20% of products).
While disparity between discounts on Prime products vs non-Prime was quite evident, it was surprisingly low for many categories. In fact, the Electronics category in the UK and the Furniture category in India witnessed sharper discounts for non-Prime products than Prime.
Top categories by additional discount include Women’s Handbags, Sports Shoes, and Pendrives in the US, Sunglasses and Tablets in the UK, and Women’s Tops, Men’s Jeans, Women’s Sunglasses, and Refrigerators in India. Top brands include Nike, Amazon Essentials, Sandisk, and 1home in the US, Oakley, Toshiba, Belledorm, and rfiver in the UK, and Adidas, Sony, UCB, and Red Tape in India.
As indicated in the following infographic, some of the most discoverable brands during the sale include Canon, Apple, Nike and Casio in the US, Sandisk, Amazon, Levi’s, and Ray Ban in the UK, and Nikon, UCB, Whirlpool, and HP in India. Discoverability here is measured as a combination of the number of the brand’s products in the top 100 ranks and the average rank of all products of the brand. Also in the infographic, is a set of products with high additional discounts during the sale.
Amazon’s competitors though aren’t ones that simply roll with the punches.
Flipkart, Amazon’s largest competitor in India (recently acquired by Walmart), announced its own Big Shopping Days sale between July 16 and July 19. On Prime Day, the company joined in with some attractive offers:
8%, 10%, and 7% additional discounts on 11%, 29%, and 16% of Electronics, Men’s Fashion, and Women’s Fashion categories, respectively.
35% off on Perfect Homes 3-seater Sofa
27% additional discount on Acer Predator Helios Gaming Laptop
25% additional discount on Sandisk 16GB Pen Drive
Propelling the Amazon Flywheel
While Amazon clearly benefits in the short-term with this sale, the long-term effect of feeding its famous flywheel is evident as well.
Amazon’s flywheel is a framework through which the company looks to build a self-feeding platform that accelerates growth over time. Attractive discounts and a broad selection of products improves customer experience, which increases traffic to the website, which attracts more merchants on its platform, who in turn broaden the selection of available products.
Sale events like Prime Day create the sort of hype needed to draw a lot of traffic to Amazon’s website, generating momentum that has a compounding effect on Amazon’s growth. Not surprisingly, more than half of the people surveyed in the US by Cowen last December said they lived in a household with at least one Prime subscription.
As Amazon’s stock traded at an all time high following Prime Day, it’s only a matter of time before the company becomes the world’s first trillion dollar company.
Check us out, if you’re interested in learning more about our technology and how we provide Competitive Intelligence as a Service to retailers and consumer brands.
Confidence amongst retailing analysts was rocked last month by two successive announcements.
H&M’s most recent quarterly report, which revealed it had accumulated over $4.3 billion in unsold inventory, shocked retail analysts. In an era of on-the-fly inventory replenishment where stocks are closely matched to sales, a spike in unsold inventory is a strong indicator of trouble ahead. The news left analysts questioning H&M’s competitiveness in the fiercely contested global apparel category, where ever-changing consumer preferences demand agility in managing inventory levels.
In the other major announcement, Toys “R” Us officially closed its doors to shoppers. The retailer’s losses continued to pile up and the chain groaned under a mountain of debt, leaving it little choice but to close down. “The stark reality is that the (chain is) projected to run out of cash in the U.S. in May,” it said in its bankruptcy filing.
While the emergence of the online shopping phenomenon hasn’t helped Toys “R” Us, its ongoing afflictions largely reflect strategic missteps that predated the online shopping boom. In a category where the shopping experience is all, the retailer failed to adapt to changing consumer expectations. The warehouse context which shaped the retailing did little to promote toys sales or communicate the sheer breadth of inventory carried by Toys “R” Us.
So, as Toys “R” Us begins to wind down its operations, the company has shuttered its online store and is channeling customers to its remaining physical retail outlets. However, prior to the closure, shoppers enjoyed some amazing bargains during their clearance sale.
H&M’s problems appear less terminal. Its management claim to have implemented a strategy to slash its accumulated inventory and reign in its aggressive store expansion strategy.
At DataWeave, we leveraged our proprietary data aggregation and analysis platform to analyze the clearance sales of both H&M and Toys “R” Us. We tracked the pricing, product categories, discounts, review ratings, stock status and more between 29-Mar and 3-Apr.
The Toys “R” Us Sale
Although the dolls and stuffed animals category carried the most products, its average discount was along the mid-range point for the sale at 28 percent. Games & Puzzles and Action Figures and NERF were the most heavily discounted categories at 40 percent and 36 percent respectively.
As anticipated, products with lower review ratings were sold at slightly higher discounts. However, even exclusive products were sold at comparatively high discounts. Not surprising, given this was effectively a clearance sale.
Hasbro, Mattel, and Spin Master were the highest represented brands during the sale, while for their part, Kid’s Furniture and Outdoor Play had fewer products participating in the sale. Other popular brands such as Fisher-Price and LEGO had a presence during the sale but offered fewer products.
Zuru was the most aggressive in offering discounts with Spin Master the least aggressive. The remaining brands offered discounts of between 30 and 36 percent.
Reports suggest that last year, toymakers Mattel and Hasbro each sold around $1 billion worth of their toys at Walmart, more than the volume they achieved selling through Toys “R” Us. Strategically, these leading brands seem to have their bases covered even though Toys “R” Us is closing down.
Interestingly, some products were seen to go out of stock during the sale week, only to be replenished a day later, as illustrated in the above infographic.
The H&M Sale
Overall, H&M’s clearance sale was more aggressive in Women’s Apparel with three times more products on offer than for Men’s Apparel. However, there wasn’t much difference between the two in terms of the discounts on offer which hovered around the 45 percent range. Women’s Tops, Cardigan’s and Sweaters offered discounts on the most products during the sale period.
Little difference was observed tactically, between how the different product categories, were handled.
We saw a significant movement of products in Women’s apparel during the week, with over 330 newly added products and close to 200 products that were effectively churned. This pattern indicates H&M achieved a faster shelf velocity for this category than for Men’s, possibly due to a more aggressive approach to the selection of items on sale.
Customer focus is key
Reports indicate that despite a series of widespread and aggressive markdowns as shown in the analysis above, H&M is struggling to sell off its mountain of accumulated merchandise. Changing consumer tastes and increasing competition seem to have taken their toll on the once agile Swedish retailer. If it is going to weather this storm, H&M needs to revisit its fast fashion approach to assortment and inventory management. The retailer would also appear to need to improve its demand forecasting expertise.
The bankruptcy filing by Toys “R” Us presents yet another lesson for eCommerce and bricks-and-mortar retailers alike, to address evolving consumer expectations and focus closely on the customer experience aspect of their business, which are supported by appropriate pricing and product assortment strategies.
At DataWeave, our technology platform enables retailers to do just that, through comprehensive and timely insights on competitive pricing, promotions, and product assortment. Check out our website to find out more!
From initially lagging well behind early high-penetration categories such as consumer electronics, books, and apparel, furniture is now emerging as a key growth category.
Online furniture purchases are growing at a rapid clip, estimated to currently be around 14 percent rate annually and is anticipated to reach 7.6 percent of total category sales in 2018.
Savvy furniture brands are becoming increasingly aware of this shift in consumer shopping patterns and are taking steps to embrace the importance of creating a seamless online customer experience consistent across all eCommerce websites.
Selling furniture online remains logistically complex. It requires the disciplined coordination across an ecosystem teeming with bricks and mortar stores, salespeople, warehouses merchants, and a network of delivery systems.
All this complexity poses challenges for brands looking to deliver a consistent brand experience for consumers across multiple eCommerce websites.
One frequent outcome of this complex ecosystem is the emergence of white labeling.
The Invasion of White Labeling in the Furniture Category
A white label product is one that is manufactured by one company only to be bundled and sold by other online merchants using different brand names. The end product is positioned as having been manufactured by the brand marketer.
These white label products are frequently sold at a significant discount, compared to more mainstream name brands in the category.
Electronics brands have often been victims of this phenomenon. Typical electronic white label products now commonplace range from radios and DVD players to computer mice and keyboards, through to TV remote controls.
Increasingly, the furniture vertical is no longer a stranger to white label packaging and marketing as well.
At DataWeave, using our proprietary data aggregation and analysis platform, we analyzed a range of factors of the furniture vertical, specifically the emerging phenomenon of white labeling.
Our analysis spanned a sample set of over 20,000 products that we tracked across the websites of two of our eCommerce customers (whom we don’t wish to name) that have a large assortment of furniture products. Let’s call these eCommerce companies Retailer A and Retailer B.
We identified white labeled products as being those that featured the exact same image between the two retailers but were sold under different brand names. Here, our AI-powered advanced image analytics platform matched the images of various products at an accuracy of more than 95%.
The following infographic summarizes our analysis.
Clearly, not only is white labeling quite prevalent here, but in almost every instance, we identified price variation. Some of the white labeled products were sold by lesser-known brands with significantly lower price points. This pricing strategy could potentially damage the customer experience for well-established consumer brand franchises in several ways.
The shopper sees through the branding exercise where the same product is repackaged and presented as having been “produced” by a different brand, potentially eroding brand loyalty.
As some 71 percent of the products studied were identified as white labeled products, this exposes the category as a whole to this risk.
The shopper may be confused by the price difference as well, undermining the brand’s carefully constructed pricing perception. The average spread of 21 percent between competing white labeled products is potentially a major source of consumer dissonance and confusion.
A Closer Look at Pricing
While the inconsistent experience potentially created by widespread white labeling is almost characteristic of the furniture vertical, other eCommerce areas such as pricing and promotion have also been demonstrated as being key influencers of the shopping experience.
Today, brands have little control over how their products are priced on eCommerce websites and are susceptible to pricing decisions taken by either the merchant selling the product or retailers themselves. Here, price change decisions have little to do with providing a consistent brand experience, as it’s not really a priority for merchants and retailers.
In a hyper-competitive retail environment, retailers often discount heavily or change prices frequently to drive sales and margins. The following infographic summarizes the differences in pricing approaches between the two retailers we analyzed.
Both retailers demonstrated quite divergent approaches in their pricing strategies. The key point of difference appeared to be Retailer B’s discount execution, which proved more aggressive than Retailer A’s, routinely exceeding the latter by five percent or more.
This discounting strategy is focused on the 40+ percentile (by price, with 100 percentile being the most expensive product), and above price bands, while both retailers displaying similar strategies to their Top 20 and Top 20 to 40 percentile ranges.
We also observe how Retailer B is more inclined to offer higher discounts on products with higher review ratings, compared to Retailer B’s strategy — a play on developing a “low price” perception among shopper.
The Consumer Experience Matters
Today, consumers expect a truly seamless shopping experience right across a brand’s entire integrated retail community, regardless of whether it is physical or digital. Consumers have evolved beyond being merely time poor and have emerged as a group of impatient shoppers, unforgiving of inconsistencies in their experience.
With retail evolving to embrace multiple consumer touch points with a brand, the practice of white labelling represents a dangerous source of potential confusion and disillusionment. This raises the degree of difficulty involved in converting website visitors into buyers. Further, inconsistent pricing between eCommerce websites, due to dissimilar pricing strategies adopted by each website, only compounds the problem for furniture brands.
Technologies like DataWeave’s Competitive Intelligence as a Service, that can provide furniture brands with timely insights on white labelled products, unauthorized merchants, and price disparity between ecommerce websites, can assist furniture brands in their efforts to better manage their online channel.
Visit our website to find out more on how we help consumer brands protect their brand equity and optimize the experience delivered to their customers on eCommerce websites!
Apparel remains one of the key battleground categories in retail today, and like in most other product categories, Amazon has made significant in-roads here. Beyond expanding the range of product offerings and brands in its marketplace, Amazon has also launched several private label brands in this vertical and looked to drive more sales as a first-party seller.
Recently, DataWeave collaborated with Coresight Research, formerly known as Fung Global Retail & Technology, a retail-focused research arm of Li & Fung Group, to publish an in-depth report revealing Amazon’s strategic approach to product assortment in its fashion and apparel category.
Our analysis focused on several critical areas, including the presence of Amazon’s private label, the demarcation between Amazon as a seller and its third-party sellers and the top brands and categories in women and men’s apparel.
We aggregated data from Amazon.com in two stages:
Firstly, we identified brands with a meaningful presence in Amazon’s clothing offering by identifying all brands included in the top 500 ranks of featured product listings for each product type in the Women’s Clothing and Men’s Clothing sections on Amazon (e.g., the Top 500 product listings for women’s tops and tees, the Top 500 product listings for men’s activewear, and so on.). This generated a total of 2,798 unique brands.
Secondly, we aggregated our data on all product listings within the Women’s Clothing and Men’s Clothing sections for each of the 2,798 brands identified previously. This returned a total of 881,269 individually listed products. This extensive list forms the basis for the highlights in Coresight’s report.
Coresight’s Analysis — Some Interesting Insights
Strategically, Amazon remains heavily reliant on its third-party sellers in the clothing category. In total, just 13.7 percent of women’s and men’s clothing products featured on Amazon Fashion are listed for sale by Amazon itself (first-party sales), while third-party sellers account for 86.3 percent of listings.
In womenswear, third-party sellers account for 85.7 percent of listings, while in menswear, they account for 87.1 percent of listings. Moreover, Amazon appears to be focusing its first-party clothing inventory on the higher-value categories. Clearly, the retailer’s reliance on third-party sellers underscores its opportunity to grow its sales of apparel volumes by bringing more of its current inventory in-house.
The analysis found 834 Amazon private-label products on Amazon website, equivalent to 0.1 percent of all clothing available through Amazon Fashion. The company’s private labels appear to be clustered tightly in specific clothing categories.
Womenswear brand Lark & Ro is by far the biggest of Amazon’s apparel private labels, as measured by the number of items.
Nike is the most-listed brand on Amazon Fashion, with 16,764 listed products spanning womenswear and menswear. Lower-price brands such as Gildan and Hanes also rank very highly in terms of the number of products listed.
Value-positioned brands that have traditionally focused on wholesaling to retailers, such as Gildan and Hanes, also rank very highly in terms of the number of products listed.
What is clear is that currently, Amazon’s clothing listings are highly diluted, with no one major brand dominating the listings.
Interestingly, casualwear and activewear clearly lead Amazon’s category rankings. Women’s tops and tees are the most heavily listed clothing category on Amazon Fashion, with 138,001 products listed.
Men’s shirts, which includes a large number of casual shirts together with polo shirts and some T-shirts, comes in second, with 109,043 products listed. Echoing the prominence of the global Nike and Adidas brands on the Amazon website, activewear has achieved a centre of gravity status as a category, accounting for 76,930 men’s activewear products and 51,992 women’s activewear products listed on the site.
Several Opportunities for Growth
Amazon Fashion remains heavily dependent on third-party sellers. It’s a fair assumption that more first-party listings would attract greater numbers of shoppers, especially Amazon Prime members. Amazon’s private-label ranges represent another potential lever for growth.
Also, the 30 most-listed brands on Amazon Fashion comprise 30 percent of all clothing products listed on the website, while just 189 brands have more than 1,000 products each listed on the website.
This data indicates the presence of major growth opportunities across the board, be it Amazon private label brands, Amazon as a seller, and for several mid-range clothing brands.
If you’re interested in DataWeave’s technology, and how we aggregate data from the Web to provide unique and comprehensive insights on eCommerce products and pricing, check us out on our website!
Following a successful Black Friday in November, the United Kingdom geared up for the 2017 Christmas season in December. Analysts estimate the total splurge in December at about £45 billion, beating last December’s record of £43 billion.
Online sales hit £1.03billion, passing the £1billion threshold for the first time and up 7.9 percent on 2016’s £954million, according to the Centre for Retail Research. The rise of online shopping together with the timing of Christmas in 2017 meant shopper footfall in physical stores was lower than in previous years as people increasingly moved to shopping online.
Total shopper numbers were 4.5 percent down on the previous year, according to research group Springboard, which may reflect the growing strength and reliability of online’s product range and delivery responsiveness.
Major online retailers though continued to pull out the big discount guns across categories in an effort to attract online shoppers on Boxing Day, the biggest sale event in December.
At DataWeave, we focused our proprietary data aggregation and analysis platform to analyze the top 500 ranked products in over 20 product categories across electronics and fashion retailers in the UK. Our analysis included several top UK retailers, which include Amazon, Argos, Currys, Tesco, Asos, Marks & Spencer, and Topshop.
The discounts in the infographic below indicate the magnitude of reduction in prices during the sale (26th Dec), compared to before the sale (19th Dec), in order to best represent the additional value derived from the sale for shoppers.
Boxing Day Sale Highlights
In electronics, while Amazon offered discounts on the most number of products, Argos was aggressive in the average size of its additional discounts.
Surprisingly, Amazon appeared to be much more conservative in the Men’s Fashion category with an average additional discount of 13.8 percent, spanning 341 products. Here, Asos deployed the most aggressive combination of high average additional discounts (36.9 percent) on a large number of products (165).
Marks & Spencer focused their targeted discounts (43.1 percent) on a tight set of Men’s Fashion products (45), while interestingly, the story almost reverses in Women’s Fashion, where both M&S (43.1 percent, 281 products) and Topshop (40.5 percent, 226 products) were aggressive in what turned out to be a critical battleground category.
Leading brands weren’t left out of the discounting action either, with the largest discount on offer going to Ruche (48.9 percent on 33.3 percent) women’s tops, closely followed by M S Collection (41.9 percent on 32.3 percent) handbags and Asos’ (37.5 percent on 21.2 percent) men’s jeans.
Most Discoverable Brands
We also analysed the most discoverable brands in each product type. This was measured as a combination of the number of the brand’s products present in the Top 500 ranks of a product type, as well as the average rank (lower the number, higher is the discoverability).
It was no surprise that Canon DSLR cameras were highly discoverable on Amazon with 90 products, along with an average ranking of 93.2, while 34 Asus laptops recorded an average ranking of 85.2. At Argos, 57 Acer laptops recorded an average ranking of 73.4 while 50 LG televisions delivered an average ranking of 124.1.
Other highly discoverable brands included MS Collection in Marks & Spencer, Apple iPhones and Tablets on Curry’s and Tesco.
The Online Retail March Continues
If we look at sales results across the world, from the United Kingdom to the United States, to Asia in countries such as India, Singapore and Indonesia through to Australia, online retail is aggressively cannibalizing traditional bricks and mortar in-store retail sales. Online retail’s demonstrated superiority in exploiting competitive intelligence and a sophisticated suite of analytics that accompany digital transactions, is surfacing in its agile discounting strategies, and its ability to continuously refresh product lines during key sales periods.
This Boxing Day in the UK, fashion proved to reveal divergent discounting strategies between retailers, while only marginal differences in approach were visible in electronics — both high volume categories around Christmas season.
Overall, December 2017 in UK marked a strong validation of online retail’s influence and we can expect a continuation of it’s ability to harness discounting with extensive product offerings, in order to lure shoppers away from in-store.
If you’re interested in DataWeave technology, and how we deliver Competitive Intelligence as a Service to retailers and consumer brands, check out our website!
Following three back-to-back mega-sale events leading up to Diwali, India’s eCommerce companies once again opened the discount floodgates heralding Christmas and New Year. This time around, Fashion was the battleground category of focus for Indian e-retailers.
Myntra launched its End of Reason Sale held between 22nd and 25th December. eCommerce behemoth Amazon too announced its own grand Amazon Fashion Wardrobe Refresh Sale on the same days, while Flipkart hit the market with its End of Year Bonanza held on the 24th and 25th of December. Paytm and Snapdeal held sale events as well, starting 23rd December. All competing sale events promised consumers up to 80 percent discounts across a range of products, especially in Fashion.
At DataWeave, we analyzed and reported on the competing pricing strategies of Amazon, Flipkart, Myntra, Paytm, and Snapdeal. In the following infographic, we look specifically only at additional discounts offered on the top 500 ranked products of over 15 product types during the sale, compared to those before the sale events went live.
Myntra Gets Aggressive
Myntra elected to discount over 84 percent of its Top 500 ranked Fashion products encompassing each product category, with an average additional discount percentage of over 25 percent offered during the sale.
A prime example of this discounting approach was the sports shoe segment, which received an aggressive additional discount of 28 percent on over 93 percent of the Top 500 ranked sports shoes. Similarly, Myntra’s additional discounts ranged from between 22 percent and 25 percent across most product types, including T-shirts, Shirts, Handbags, Jeans, Skirts, Sunglasses, and Watches. The fashion e-retailer’s private label brands enjoyed attractive reductions in prices, which include Hrx and Roadster, along with other brands like Red Tape, Nike, and Puma.
Amazon Discounts To A Different Beat
Amazon discounted 35 percent of its Top 500 ranked Fashion products in each product type, with an average additional discount percentage of 12.5 percent during the sale. Given Amazon’s track record of dynamic pricing, this was relatively conservative.
Overall, additional discounts on Amazon ranged between 4 percent and 16 percent across all product types in Fashion. Top brands discounted on Amazon included Adidas, Fastrack, Hush Puppies and Ray-Ban.
Flipkart Joins The Party
Flipkart too joined the End of Year discount action with several attractively positioned offers, exceeding those featured on Amazon. Flipkart discounted over 65 percent of its Top 500 ranked Fashion products in each product type, with an average additional discount percentage of over 14 percent during the sale.
Additional discounts promoted on Flipkart ranged between 8 percent and 22 percent across all Fashion product types, while some of the top discounting brands included Dkny, Metronaut and United Colors of Benetton.
Conspicuously, other Indian e-retailers like Paytm and Snapdeal chose not to join in the price war. Snapdeal, especially, has consistently offered only moderate additional discounts during recent sale events, choosing to focus more on other areas of improving the user experience for their shoppers.
Strategic Focus On Profitability
In contrast to the profit-sapping Diwali sale season, characterized by steep discounts across all product categories, this end of year sale was more concentrated, largely honing in on Fashion. From a strategic and shareholder perspective, limiting the discounting action to Fashion insulated the retailers’ bottom line from another major profit hit.
Myntra determinedly reaffirmed its leadership status in the Fashion category, with its highly aggressive discounting strategy. This was well received by shoppers, who spent a staggering ₹5 crore in only the first five minutes of the sale.
Flipkart opted to double down this time around with attractive offers on its own eCommerce platform as well. The e-retailer, currently locked in a battle with Amazon for leadership in India’s eCommerce sector, had acquired Myntra in 2014 in a bid to strengthen its position in the fashion category.
Amazon, intriguingly, opted for a more conservative approach to its end of year sale than we are used to witnessing from the eCommerce giant. As we enter the new year, and kickstart yet another cycle of aggressive e-retail promotions in India, there will be ample opportunities to see if this is evidence of a rethink in Amazon’s approach to pricing in India.
If you’d like to know more about DataWeave’s technology, and how we provide Competitive Intelligence as a Service to retailers and consumer brands, check out our website!
Commencing on the 11th of November and ending just a few days ago on the 12th of December, Southeast Asia’s biggest sale event, the Lazada Online Revolution sale rewrote the record books.
This mega-shopping event is held simultaneously across six Southeast Asian countries, spanning Singapore, Malaysia, Thailand, Indonesia, the Philippines and Vietnam and was bookended by its two biggest sale days, on 11.11 and 12.12.
In an earlier blog post, we published a highly detailed analysis of the sale on 11.11, using DataWeave’s proprietary data aggregation and analysis platform. This post zoomed in on the pricing and product strategies of Lazada and its competitors in Singapore and Indonesia.
On the 12th December, Southeast Asian shoppers shattered all retailing expectations by reportedly spending a record-breaking $250 million. This was double both this year’s 11.11 sale and last year’s 12.12 sale. According to Forbes, the 12.12 sales became such a hit that Indonesia even designated the day to be its National Online Shopping Day, or Harlbonas.
At the end of the sale event on 12th December, DataWeave assimilated all the data we collated throughout the Online Revolution sale and examined pricing trends across the entire span of four weeks, exploring each retailer’s strategy by brand, by category, and by product type.
We aggregated pricing information on the Top 500 ranked products of over 20 product types featured on each website (Lazada, ListQoo10, and Blibli), spread across the critical Electronics and Fashion categories, covering over 120,000 products in total.
Online Revolution — Singapore
Interestingly, one of the trends that became immediately apparent, was the relatively stable track of the average absolute discounts in Electronics, Men’s Fashion, and Women’s Fashion. No significant spikes or drops were evident throughout the duration of the entire sale season.
Similarly, the number of discounted products remained relatively stable. However, in Electronics, there was a conspicuous dip in the number of discounted products, which occurred on the 21st of November. Aside from this anomaly, even the number of products discounted remained relatively stable. The other interesting phenomenon was an uptick in the number of discounted products on the 15th of December, after the Online Revolution sale — something counterintuitive.
When we explored the behaviour of the average MRP of discounted products, we noticed a sharp dip on the 21st of November. Clearly, prices were increased specifically on higher-priced electronic products.
Comparing these numbers with those of ListQoo10’s, who were forced to adopt a more aggressive stance on pricing to stay competitive through this period, we once again see a very consistent discount percentage throughout this period. The average discount in men’s fashion, however, showed a slight upward trend during this period.
ListQoo10’s number of discounted products in Electronics dipped as well on the 21st of November, demonstrating the retailer’s ability to dynamically react to competitor strategies. This can be evidence of a robust market monitoring system.
Returning to Lazada, DataWeave identified several product types displaying a significant variation in average discounts through this period. These included men’s shorts, women’s shoes, men’s Jeans, Laptops, DSLR Cameras, and women’s T-shirts.
Once again, our analysis pointed to substantial competitive activity around the 21st of November, together with a second significant dip in discounts on men’s shirts in the period around 5th December. Discounts on women’s shoes, by contrast, proved to be a roller coaster throughout the entire sale period.
Some of the brands with high variation through this period were Lenovo Laptops, Levi’s T-shirts, Adidas Women’s Shoes, Seiko Watches, and Sony Phones.
Discounting activity by these brands appeared to be all over the place during this period, without any discernible pattern or structure. While Sony predictably lowered discounts on its phones after 12.12, Levi’s increased its discounts in the same period
Online Revolution — Indonesia
Moving on to Indonesia, we once again witnessed a similar approach to average discounting by category as we saw emerge in Singapore. At a category level, the retailer evidently opted for trading within a narrow discount band across the sale period rather than attempting to inject an overly dynamic discounting approach into their sale execution.
This is not to say there were not some surprises in store with the number of discounted products in Indonesia. In electronics, there was a noticeable dip in the number of discounted products just ahead of the 12.12 sale. The number of discounted products then surprisingly surged after the 12.12 sale, in combination with a slight reduction in average discount percentage during the period.
In comparing Blilbi, Lazada’s main competitor in Indonesia, we see a fairly consistent discounting level throughout the sale period, although markedly lower than those rolled out by Lazada across its three core categories.
This approach held true even for the number of discounted products. Blibli seems to have been content to take a backseat to Lazada during the heavily promoted Online Revolution sale period, rather than attempting to compete aggressively in any single category.
It will be interesting to see if Blilbi is content to repeat this strategy in 2018 as it effectively surrenders the discounting high ground to Lazada during the peak sales period. While this strategy may yet be proven to have paid off in terms of profitability, it may have undesirable consequences for Blilbi’s brand and share performance in the longer term.
Returning our focus to Lazada in Indonesia, some product types showed major variation through the sale period, specifically DSLR Cameras, which dipped significantly approximately a week out from the 12.12 sale. However, compared to Singapore, Indonesian discounts by product types appeared relatively more stable, except a few dips prior to 12.12.
Three distinct discounting strategies appears to have been adopted by participating brands. Some, such as Electrolux (Refrigerators), opted for a comparatively stable discounting approach. Others, like Apple, increased prices through the sale period, while Alienware, reduced prices through the sale period.
In particular, Apple’s pricing approach to its iPhones was surprising, given its strong partnership with Lazada during this Online Revolution sale. Yet another example where the marketing hype failed to translate into an aggressive discounting strategy.
More Talk Than Walk
For Lazada, the Online Revolution sale proved to be a triumph, effectively extending its record-breaking streak with USD 250 million in sales on 12.12 alone. However, on parsing through the pricing across the entire month of the sale, there is clearly no dramatic increase in discounts either on 11.11 or on 12.12 — some anomalies notwithstanding.
This goes to show that much of the sales is driven by hype, more than the additional value of discounts. To be fair, 11.11 and 12.12 hosted discounts on some of the more premium products in the assortment, while discounts on most of the mid-range products remained consistent. While some competitors like ListQoo10 chose to stay competitive, so as not to lose out significantly on their customer base and market share, others like Blilbli chose to sit and watch, and pick up on what’s left after the sale.
This year’s Online Revolution has set the bar high for South East Asian retail, and going by how the event has grown over the last few years, few would be surprised if we witness another record braking sale in 2018.
If you’re interested in DataWeave’s technology, and how we provide Competitive Intelligence as a Service to retailers and consumer brands, check us out on our website!
While the Thanksgiving weekend sale, which includes Black Friday and Cyber Monday, is famous for attractive offers across all consumer categories, it remains better known for its discounts on Electronics and Fashion. Consumer goods, traditionally, have evaded much the hype.
This year, notwithstanding notoriously slim margins, consumer goods and grocery retailers and brands joined Electronics and Fashion in offering sharp discounts on select products in an attempt to carve out increased market share.
In the past, discounts on consumer packed products have been to drive increased store traffic during the holiday season. Increasingly, however, Thanksgiving has emerged as a viable opportunity for grocers to recruit online shoppers as well and build out their franchise.
Online Grocers Make Their Move
Faced with the holiday rush, large numbers of shoppers are proving to be relaxed about trusting the retailer to bag up and deliver their holiday feasts and treats. Grocers themselves have taken the strategic decision to boost their online shopping presence this year.
They geared up to support their new holiday presence with aggressive price cuts designed to cut through the holiday sales clutter and make direct appeals to a newly-in-play online shopper pool. So transparent was this commercial decision, that many retailers experienced sharp drops in their share prices as industry analysts anticipated the retailers’ new discount-driven strategy.
Tracking The Numbers
At DataWeave, using our proprietary data aggregation and analysis platform, we have been tracking, through November, pricing and product information of the Top 1,000 ranked consumer goods products in over 10 product types featured on Amazon Prime, Walmart, Target, Costco, Kroger, Safeway, and Whole Foods, across up to six zip codes each, distributed across the country.
DataWeave’s major focus was to compare the three main days of the Thanksgiving weekend; Thanksgiving Day, Black Friday, and Cyber Monday. We performed an in-depth analysis of discounts offered across product types and brands, together with how aggressively dynamic retailers were in both their pricing strategy and in the products they displayed.
In analyzing this major sale event, we observed an extensive range of products enjoying high absolute discounts, but with no additional discounts during the sale, i.e. prices remained unchanged between the period prior to the sale and during each day of the sale, even though high discounts were advertised. The following infographic highlights some of the products where this phenomenon was observed.
As a result, we focused our analysis only on the additional discounts offered on each day of the sale, compared to the period prior to the sale (we considered 11.21), in order to accurately illustrate the true value shoppers enjoyed during these sale days.
The following infographic reveals some interesting highlights from our analysis, including the level of additional discounts offered to shoppers, the top brands featured, and the number of dynamic price changes implemented during the sale. All prices analysed are in USD, and all discount percentages represent average values across all zip codes, analyzed for individual retailers.
In contrast to Amazon Prime, Costco, and Kroger who opted to run with deep discounts on a limited range of products, retailers such as Target and Walmart chose to offer only marginally higher additional discounts but across a large number of products. Others like Safeway adopted a safer approach, combining low discounts on a modest range of products.
Overall, our analysis discovered little variation in discounts offered across each of the three sale days, with the only enduring trend being a marginally higher discount percentage implemented on Cyber Monday across all retailers.
Categories significantly discounted across retailers included Personal Care, Deli, Dairy & Eggs, and Babycare products. Stove Top, Martinelli, Colgate, Dove and Hillshire Farm emerged as the leading brands to adopt a more aggressive discount approach.
While most of the products offered across each of the three peak holiday sale days were comparatively constant (few new products featured amongst the Top 500 ranks), there were a number of conspicuous exceptions. Amazon Prime (19 percent on Cyber Monday), Whole Foods (15 percent on Thanksgiving), and Kroger (12 percent and 11 percent on the first two days of sale respectively), elected to refresh a significant portion of their Top 500 ranked product assortment.
Across the entire Thanksgiving week, we saw Target, Amazon Prime, and Kroger all highly active in changing prices to stay competitive. Our analysis of these retailers showed more than 1.6 price changes for each price-changed product. While these were implemented on roughly 20 percent of their assortment, itself a significant proportion, the average price variation for each of these retailers was also on the higher side of expectations. In contrast, the other retailers adopted a far more conservative approach to dynamic pricing.
Consumer Goods Walk The Discount Talk
In a year when Amazon acquired Whole Foods to forever merge the dynamics of offline and online grocery retail, aggressive discounting by several retailers in specific product categories, combined with high visibility brands, has carved out a new profile for CPG retail.
Grocers are eyeing a future where online shopping becomes a prime feature of their retail franchise. Amazon for its part demonstrated its prowess in discounting strategy, and its ability to implement a dynamic pricing strategy in tandem with a refreshed Top 500 product assortment.
Other retailers are not far behind, as the use of market and competitive intelligence technologies pick up steam across the board. In today’s digital economy, data can be the biggest competitive advantage for a retailer, and retail technology providers like DataWeave have upped their game to deliver highly unique and sophisticated data and insights to meet this demand.
Visit our website, if you’re interested in DataWeave and how we provide zip-code level Competitive Intelligence as a Service to retailers and consumer brands.
Fashion has always been one of the great engines of retail, and two of its iconic sale events are Thanksgiving and Black Friday. While Black Friday was traditionally an in-store shopping event, a large number of shoppers have migrated online taking much of the sales action with them.
Despite shoppers typically liking to be able to touch and feel fashion and apparel products prior to purchasing them, the convenience of online shopping combined with time-poor shoppers returning to work after their Thanksgiving break has triggered changes to consumer behavior. Today, the retail narrative has shifted to focus on online, with this year’s Thanksgiving weekend turnover up 6.8 percent from last year.
At DataWeave, using our proprietary data aggregation and analysis platform, we have been tracking the pricing and product information of the Top 500 ranked Fashion products across 15 product types on Amazon, Walmart, Target, Bloomingdales, JC Penney, Macy’s, Neiman Marcus, and Nordstrom.
Our primary focus was to compare the three key days of the Thanksgiving weekend: Thanksgiving Day, Black Friday, and Cyber Monday. We performed an in-depth analysis of discounts offered across product types and brands, together with how dynamic retailers were in both their pricing strategy and products displayed.
In analyzing these monster sale events, we observed a range of products sneaking through to enjoy high absolute discounts, but offer no additional discounts during the sale, i.e. prices remained unchanged between before the sale and during each day of the sale, even though high discounts were advertised. The following infographic highlights some of the products where this phenomenon was observed.
Having identified the aggressive use of high but unchanged absolute discounts among the retailers during the sale, we focused our analysis on the additional discounts offered on each of the days of the sale, compared to before the sale (we considered 11.21), in order to more accurately reflect the true value these sale events deliver to American shoppers.
The following infographic provides some interesting insights from our analysis along several perspectives, including additional discounts offered, top brands, quality of product assortment, number of price changes, and more. All indicated prices are in USD.
Our analysis illustrated how aggressive Target was in its strategy for discounting fashion, compared to most other retailers, especially on Thanksgiving and Black Friday. Interestingly, while Macy’s offered reasonably attractive discounts across all product types, it chose to offer them on a much larger product set than any other retailer.
Overall, the level of discounts, together with the number of products they were offered on, shows no dramatic change for each retailer over the three-day sale period.
With Neiman Marcus however, we observed a unique pattern. Sharp discounts were offered on Thanksgiving and Black Friday, which were subsequently rolled back completely on Cyber Monday. This represents a clear holiday pricing and discount strategy, albeit conducted on a comparatively compact and highly targeted set of products.
Other sales discounting phenomena we observed include major discounts on Sunglasses, Shoes, Skirts, and T-shirts across all retailers, clearly representing battleground categories, while some of the top brands offering attractive discounts include Ray Ban, Oakley, Levi’s and Nike.
Another relatively constant factor across each of the sale days was the average selling price of respective retailers. This parameter indicates how premium each retailer’s product mix is, providing another perspective on each retailer’s customer segment targeting strategy.
As expected, Target, Walmart and JC Penney housed the more affordable set of products (average selling prices of $25, $31, and $45 respectively). At the other end of the premium spectrum, Neiman Marcus — home to luxury brands and products — adopted a more premium product assortment (average selling price between $820 and $914).
In fashion, presenting a fresh assortment consistently is key to customer retention, and Amazon leads the pack in this regard, with a product churn rate of 50% in the top 100 ranks each day. Contrast that with Walmart and Target, who follow a more traditional approach, with a largely static set of options to choose from in its top ranks.
Most of the retailers we analysed implemented several price changes to large percentages of their product sets. Macy’s and Walmart were at the forefront of this dynamic pricing activity. While Bloomingdales too made over 1,300 price changes, the average magnitude of these changes proved to be very high, at 206 percent.
Fashion Fast-Forwards Its Online Sales
While the memories of frantic shoppers tussling over fashion and apparel items on Black Friday still linger, they are fast receding as online fashion sales turnover goes from strength to strength. Shoppers are firmly placing long, winding queues in their rearview mirror and embracing the digital shopping cart more with each passing year, as spotlighted this Thanksgiving sale weekend.
Sunglasses, Shoes, Skirts, and T-shirts emerged as key battleground categories for retailers over the weekend, while individual retailers displayed diverse approaches to capturing and retaining market share with their target demographic — quite assuredly while using modern retail technologies that help develop and execute on competitive strategies.
As retailers move into the Christmas sales phase it will be fascinating to discover how they are evolving their ability to dynamically change pricing, refresh product categories and focus their shopper promotions.
Visit our website, if you’re interested in DataWeave’s technology and how we provide Competitive Intelligence as a Service to retailers and consumer brands.
Alibaba may have raked in some $25 billion on Singles’ Day in the largest one-day sales turnover ever. In the Western world, however, Black Friday remains an economic force.
This Black Friday, American shoppers spent a record $5 billion online in just 24 hours, representing a 16.9 percent increase in dollars spent online compared with last year.
The sale period, though, comprises of Thanksgiving Day and Cyber Monday as well — each generating over a billion and half dollars in online sales this year.
Cyber Monday has especially been a popular day for buying online, as people head back to work after the long weekend, making a physical visit to the stores to pick up deals less manageable during the day.
However, the idea of the Thanksgiving weekend as a single shopping event was laid to rest this year.
It’s Now Black November
Online sales from November 1st through the 22nd totalled almost $30.4 billion this year, driven by deals available throughout the month on eCommerce platforms. In fact, every single day in November so far saw over $1 billion in online sales, creating a new paradigm for both shoppers and retailers, in stark contrast to the brick-and-mortal retail driven Black Friday sale events of the past.
Several online retailers began offering attractive discounts from the beginning of November, specifically on “Black Friday Deals” pages of their websites.
At DataWeave, using our proprietary data aggregation and analysis platform, we have been tracking, through November, pricing and product information of the Top 500 ranked Electronics products across 10 products types on Amazon, Walmart, Target, Best Buy, and New Egg.
We also took a few snapshots of the products and discounts offered on the “Black Friday Deals” pages of Amazon and Walmart. We saw both websites offering deep absolute discounts in Electronics (40.1 percent on Amazon, 30.4 percent on Walmart) on over 400 products each day.
Moreover, these discounts weren’t restricted to static product sets. 73.2 percent (Amazon) and 30.6 percent (Walmart) churn of products was observed on these pages each day, providing shoppers with a steady stream of attractive discounts on new products every day.
Our major focus, though, was to compare the three main sale days of the Thanksgiving weekend. We performed an in-depth analysis of discounts offered across product types and brands, as well as how dynamic retailers were in both the pricing and products displayed — all of these, across Thanksgiving (11.23), Black Friday (11.24) and Cyber Monday (11.27).
We looked specifically only at additional discounts offered on each of the days of the sale, compared to before the sale (represented by products and its prices on 11.21).
Overall, we discovered that the level of discounts, together with the number of products they were offered on, does not change dramatically across all 3 days. Some exceptions include –
Higher number of additionally discounted products on Amazon and Walmart on Cyber Monday
Lower additional discounts offered by Best Buy on Cyber Monday
Lower number of products additionally discounted on New Egg on Thanksgiving and Black Friday.
Discounting strategies across most retailers converged on significant discounts on Pendrives, Smartwatches, DSLR Cameras, and Mobile Phones, while some of the top brands that offered attractive discounts include Apple, Fossil, Canon, Nikon, Sandisk, and HP — across a range of product types.
While the average selling price (indicative of how premium the product mix is) for each retailer did not change significantly across each of the featured sale days, there was some variation at a product type level, with Laptops and Digital Cameras displaying some variation in average assortment value across Target, Walmart, and New Egg.
Perhaps the most interesting insight provided by the analysis is just how different each retailer is in its approach to changing its prices. Over the entire week (11.21 to 11.27), Amazon made over 3,600 price changes on over 50 percent of its consistently-top-ranked products. Compare that to Target’s 289 price changes on 30 percent of its products.
While the average magnitude of price change on Amazon is 27 percent, Best Buy has been far more aggressive with the magnitude of its price adjustments (47 percent), even if it has implemented fewer price changes. Amazon clearly leads the industry here, with its continual focus on employing advanced retail technologies that enable automated, optimized price changes designed to ensure its products are competitively priced.
How Strategic Is Retail Pricing?
Another aspect DataWeave explored was whether e-retailers sometimes increase their prices in the lead-up to a sale, only to reduce them during the sale, enabling them to advertise larger discounts. We did observe that all e-retailers effectively increased their prices on a discrete and small set of products prior to their sale. For the purposes of our analysis, price increases before the sale was calculated as an increase in price between 11.14 and 11.21.
Highlights of our analysis include the discovery that Best Buy increased its prices in Electronics significantly on a small selection (3.5 percent) of its product range prior to the sale, only to reduce those prices immediately during the Thanksgiving weekend sale.
While Amazon proved not to be as aggressive in the magnitude of this activity as Best Buy, this phenomenon was observed across a larger portion of Amazon’s assortment (6.7 percent)
Online is Now More Important Than Ever
While the legend and aura of past Black Friday sale events, complete with long overnight queues and highly publicized stampedes, is ebbing away, in lock-step with the dwindling numbers of store footfall this year (down 2 percent), the Thanksgiving sale season is set for a new transformation, following the growing number of shoppers preferring to shop online.
A survey by the National Retail Federation found that 59 percent of shoppers plan to shop online this year, marking the first time that online has emerged as the most popular choice for America’s shoppers.
With an extended sales season to offer discounts, and moving into Christmas, it has become increasingly important for retailers to monitor and react dynamically to their competitors’ pricing, product and promotional activities. Without the ability to track, react, and tweak in real time, retailers risk having their competitive position eroded, dramatically impacting both sales and retail margins.
Leading eCommerce retailers such as Amazon, and evolving retailers like Walmart have embedded these systems into their overarching strategy and operations, while others are condemned to play catch up.
As this fascinating cycle of the sale season ends, and retailers crunch their numbers to assess their comparative performance, sights are now set on Christmas to extend this sale extravaganza.
Visit our website, if you’re interested in DataWeave’s technology and how we provide Competitive Intelligence as a Service to retailers and consumer brands.
That’s the volume of sales Alibaba generated on 11.11, or Singles Day. This mammoth event, decisively the world’s biggest shopping day, dwarfed last years’ Black Friday and Cyber Monday combined.
This year, the anticipation around Singles Day was all-pervasive, and the sale was widely expected to break all records, as more than 60,000 global brands queued up to participate. By the end of the day, sales topped $25.3 billion, while shattering last year’s record by lunchtime.
It’s an astonishing feat of retailing, eight years in the making. When Alibaba first started 11.11 in 2009, they set out strategically to try and convert shopping into a sport, infusing it with a strong element of entertainment. “Retail as entertainment” is a unique central theme for 11.11 and this year Alibaba leveraged its media and eCommerce platforms in concert to create an entirely immersive experience for viewers and consumers alike.
From a technology perspective, the “See Now, Buy Now” fashion show and the pre-sale gala seamlessly merged offline and online shopping so viewers tuning in to both shows can watch them while simultaneously shopping via their phones or saving the items for a later date.
The eCommerce giant also collaborated with roughly 50 shopping malls in China to set up pop-up shops, eventually extending its shopper reach to span 12 cities.
Of course, attractive discounts on its eCommerce platforms were on offer as well.
Deciphering Taobao.com
At DataWeave, we have been analyzing the major sale events of several eCommerce companies from around the world. During Singles Day, when we trained our data aggregation and analysis platform on Taobao.com (Alibaba’s B2C eCommerce arm), and its competitors JD.com and Amazon.ch, our technology platform and analysts had to overcome two primary challenges:
1. All text on these websites were in Chinese
All information — names of products, brands, and categories — were displayed in Chinese. However, our technology platform is truly language agnostic, capable of processing data drawn from websites featuring all international languages. Several of our customers have benefited strategically from this unique capability.
2. Discounted prices were embedded in images on Taobao.com
While it’s normal for sale prices to be represented in text on a website (relatively easy to capture by our advanced data aggregation system), Taobao chose to display these prices as part of its product images — like the one shown in the adjacent image.
However, our technology stack comprises of an AI-powered, state-of-the-art image processing and analytics platform, which quickly extracted the selling prices embedded in the images at very high accuracy.
We analyzed the Top 150 ranked products of over 20 product types , spread across Electronics, Men’s Fashion, and Women’s Fashion, representing over 25,000 products in total, each day, between 8.11 and 12.11.
In the following infographic, we analyze the absolute discounts offered by Taobao on 11.11, compared to 8.11 (based on pricing information extracted from the product images using our image analytics platform), together with an insight into the level of premium products included in their mix for each product type, between the two days of comparison.
Unexpectedly, we noticed that each day, ALL the products in the Top 150 ranks differed from the previous day — a highly unique insight into Taobao’s unique assortment strategy.
Counter-intuitively, absolute discounts across all categories were considerably higher on 8.11 than on 11.11, even if it were for a marginally fewer number of products. The number of discounted Electronics products on sale rose on 11.11 compared to 8.11 (124 versus 102 respectively), while there was little movement in the number of discounted Men’s Fashion(55 versus 57) and Women’s Fashion (35 verses 27) products.
Taobao targeted the mobile phone and tablets segment with aggressive discounts (21.0 percent and 18.2 percent respectively), compared to the average Electronics discount level of 7.7 percent.
Interestingly, the average selling price drifted up for Electronics on 11.11 compared to 8.11 (¥4040 versus ¥3330). Men’s Fashion dropped to ¥584 from ¥604 while prices for Women’s Fashion was stable.
It’s clear that even with all the fanfare, Singles Day didn’t produce the level of discounts that one might have expected, indicating that purchases were driven as much by the hype surrounding the event as anything else.
How did Alibaba’s Competitors Fare?
While Taobao was widely expected to offer discounts during Alibaba’s major sale event, we looked at how its competitors JD.com and Amazon.ch reacted to Taobao’s strategy.
As over 80 percent of top-ranked products were consistently present in the Top 150 ranks of each product type on these websites, we analyzed the additional discounts offered during 11.11, compared to prices on 8.11.
Broadly speaking, both Amazon.ch and JD.com appear to have elected not to go head to head with Taobao on specific segments. JD.com’s discount strategy was spearheaded by Sports Shoes (22.1 percent) and Refrigerators (14.8 percent) while Amazon.ch featured TVs (15.3 percent) and Mobile Phones (10.2 percent).
The average additional discounts offered by Amazon.ch and JD.com in Electronics (8.4 percent) was slightly above Taobao’s overall absolute discount (7.7 percent). TCL was aggressive with its pricing on both websites, offering over 20% discount on almost its entire assortment.
Surprisingly, JD.com swamped Amazon.ch’s number of additionally discounted products, across all three featured categories although this may be partially explained by Amazon.ch electing to adopt a significantly more premium price position in both Men’s and Women’s Fashions compared to JD.com, while remaining roughly line ball on Electronics.
Jack Ma’s “New Retail”
Interestingly, JD.com wasn’t far behind Taobao in terms of sales, clocking up $20 billion in revenue, and sparking an interesting public debate between the two eCommerce giants extolling their respective performances.
Singles Day is one of the pillars of Jack Ma’s vision of a “New Retail” represented by the merging of entertainment and consumption. Ma’s vision sees the boundary between offline and online commerce disappearing as the focus shifts dramatically to fulfilling the personalized needs of individual customers.
Hence, Alibaba’s Global Shopping Festival should be understood as not just a one-day event that produces massive revenue, but as a demonstrable tour de force of Alibaba’s vision for the future of retail. One thing is certain — as competition heats up between Chinese retailers, we can be prepared for another Singles Day shoot-out sale next year that one-ups the staggering sales volumes this year.
If you’re intrigued by DataWeave’s technology, check out our website to learn more about how we provide Competitive Intelligence as a Service to retailers and consumer brands globally.
Lazada’s signature event, Online Revolution, is a month-long sale extravaganza that commenced with a Mega Sale on 11 November, and culminates in an End-Of-Year sale on 12 December. The shopping event is held across six southeast Asian countries — Singapore, Malaysia, Thailand, Indonesia, the Philippines and Vietnam — making it the region’s biggest retail event.
Lazada Group’s chief executive officer Maximilian Bittner observed, “We aim to provide Southeast Asia’s rapidly growing middle-class the access to a wide range of products with deals and discounts that were previously available only abroad or in the capital cities.”
On 11.11, the first Mega Sale, shoppers took advantage of great deals, ordering 6.5 million items (nearly doubling last year’s tally), resulting in sales of US$123m, annihilating last year’s takings by a whopping 191 percent.
At DataWeave, our proprietary data aggregation and analysis platform enabled us to seamlessly analyze and compare Lazada’s discounts during 11.11 with those of its competitors. We focussed specifically on two markets — Singapore and Indonesia. While the sale itself is Lazada’s, we looked at its immediate competitors as well, to study how competitively they position themselves during Lazada’s sale.
For our analysis, we aggregated pricing information on the Top 500 ranked products of over 20 product types on each website, spread across Electronics and Fashion, covering over 120,000 products in total.
11.11 — Singapore
In our analysis, we scrutinized the additional discounts offered by Lazada, ListQoo10, and Zalora during the sale period, compared to prices leading up to the sale. As today’s shoppers often encounter deep discounts on several products even on normal days, our analysis of additional discounts offered during the sale more accurately reflects the true value of the sale event to shoppers.
In the following infographic, all prices are in Singapore Dollars, and additional discounts are the percentage reduction in price on 11.11 compared to 10.11.
Lazada’s discounting strategy was more focused on Fashion rather than Electronics. However, Lazada didn’t have it all its own way with Zalora providing comparably high discounts, enabling it to compete effectively, especially in Women’s Fashion (16.2 percent on 406 products).
Zalora actually exceeded Lazada in the number of additionally discounted products on offer (Zalora 406, Lazada 347). ListQoo10 did not match either Lazada or Zalora’s level of discounting.
While Lazada held a more premium, high-value product mix in Electronics compared to ListQoo10, it chose to target the more affordable segment in Fashion, with both ListQoo10 and Zalora displaying a higher average selling price in each category.
Interestingly, Lazada refreshed very few of its Top 500 products during the sale, limiting new options to choose from for its shoppers. On the other hand, Zalora refreshed 22.5 and 22.8 percent of its products in men’s and women’s fashion respectively.
11.11 — Indonesia
Using a similar methodology to our Singapore analysis, we analyzed Lazada’s promotions against Blibli and Zalora, three of the top eCommerce websites in the region. In the following infographic, all currencies are in Indonesian Rupiah.
As with its Singapore strategy, Lazada targeted Fashion as the lead category for discounts in Indonesia. It offered steep discounts in both Men’s and Women’s Fashion (around 18 percent in each) across a large number of products (550 and 776 respectively). While Zalora matched and occasionally exceeded the discounts offered by Lazada, it did so across a significantly smaller range of additionally discounted products.
Surprisingly, Electronics were de-emphasised in Indonesia (4.1 percent compared to 9 percent in Singapore).
Compared to the market leaders Lazada and Zalora, Blibli struggled to be competitive from both an absolute discount level and a product assortment perspective.
Like in Singapore, Lazada looked to be targeting the affordable value end of the product mix spectrum across all categories, and introduced very few new products in its Top 500 ranks.
Zalora had a healthier churn rate of 14.6 percent and 18.1 percent in Men’s and Women’s Fashion, compared to Lazada’s 9.1 percent (Electronics), 10.7 percent (Men’s Fashion) and 10.8 percent (Women’s Fashion).
It’s Not Just About Discounts
Lazada’s ‘Fashion First’ targeting strategy creates an effective tie-in to its broader model of surfing the convergence wave between entertainment and eCommerce, something unique to southeast Asia.
Together with sumptuously attractive discounts, major sale events in South East Asia are fast becoming characterized by entertainment. By launching Southeast Asia’s first star-studded eCommerce TV show, Lazada continues to be the region’s eCommerce innovator, following in the footsteps of its pioneering parent company, Alibaba.
While time will tell how effective Lazada’s strategy ultimately proves to be, together with Alibaba, it has set up a fascinating and uniquely Asian retail sale model. No doubt another milestone will be set on 12.12 when the Online Revolution Mega Sale returns with even greater deals. At DataWeave, we’ll be sure to analyze that sale as well and bring you all its highlights.
In today’s rapidly evolving online and mobile worlds, few things encapsulate the competitive nature of the online retail battlefield like the Black Friday sales season. With this year’s Black Friday and Cyber Monday sale events just around the corner, 2017 promises another titanic tussle between contenders.
The holiday shopping season commences on Black Friday, November 24, and continues through much of December. Anticipating the sales season, many retailers are already offering discounts on several key categories and anchor products, providing a sneak peek into what we can expect towards the end of the month.
While traditionally, Black Friday sales were dominated by brick and mortar retail stores, with the odd shopper stampede not unheard of, retail dynamics have changed in the recent past. Online sales now consume a larger proportion of Black Friday spending, and for the first time, consumers are expected to spend more online in the 2017 holiday season than in-store.
In anticipation of this mammoth sale event, we at DataWeave trained our proprietary data aggregation and analysis platform on several major US retailers to understand the competitive market environment before the sales kick off.
Between the 15th and 29th of October, we tracked the prices of the top 200 ranked products each day in the Electronics and Fashion categories across several major retailers. For Electronics, we analyzed Amazon, Walmart, Best Buy, and New Egg, while Amazon, Walmart, Bloomingdales, Nordstrom, Neiman Marcus, New Egg, and JC Penney provided our insights into the pivotal Fashion category. Product types analyzed include mobile phones, tablets, televisions, wearables techs, digital cameras, DSLRs, irons, USB drives, and refrigerators in Electronics, and T-shirts, shirts, shoes, jeans, sunglasses, watches, skirts, and handbags in Fashion.
Automated Competitive Pricing Is the New Norm
With the accelerated evolution of online commerce, retailers have increasingly harnessed the power of competitive data to drive changes on the go to their pricing, product assortment, and promotional strategy. During sale events, however, these numbers spike significantly. Amazon famously made 80 million price changes each day during 2014’s Christmas Season sale. Similarly, even on normal days some retailers have adopted the tactics of changing their product pricing more frequently than others, in their quest to stay competitive and build their desired price perception amongst shoppers.
In our analysis of price changes, we considered the set of products that ranked consistently in the Top 200 from the 20th to the 25th of October. We identified the number of price changes together with the number of products affected by price changes that were implemented by the retailers.
As anticipated, Amazon led the way with 508 price changes on 236 products in the Electronics category during the period compared to Walmart’s 413. By comparison, New Egg’s 95 price changes trailed the field by a significant margin and illustrate the tactical advantage Amazon’s dynamic pricing technology confers. However, the price variation (8.0%) of Amazon’s was also the lowest of the four retailers included in the study, showing that Amazon makes short, sharp tweaks to its pricing at a higher frequency than its competitors.
By comparison, the Fashion category demonstrated a much lower level of price changes than Electronics, albeit with significantly higher price variations. Walmart leads the pack, adopting an order of magnitude greater number of price changes across a significantly larger number of products compared to the majority of its competitors.
Product Mix Suited to Target Market Segments
While competitive pricing is one strategy for attracting new customers and retaining existing ones, the selection of products featured in a retailer’s inventory is just as important. Ensuring a disciplined product assortment, which caters exclusively to a retailer’s target market segments is key. While some retailers such as Walmart choose to house a more affordable range of products, Neiman Marcus and Bloomingdales target the more premium segment of shoppers.
It is clear from the data that Walmart has aligned its pricing strategy to support its affordability pitch to its shopper base, while Neiman Marcus and Nordstrom use pricing to juggle the demands of a more premium inventory with perceptions of price competitiveness.
Product Movement In The Top 200
Much of a retailer’s sales performance comes down to how effectively it maintains the optimal mix of reassuring bestsellers complemented by attractive new arrivals. Sound product assortment clearly provides shoppers with a variety of options each time they visit the retailer’s website. To achieve this balance, retailers typically employ their own, unique algorithm that ranks products in their listings based on several factors, including price range, discount offered, review ratings, popularity and promotions by brands.
To study this, we evaluated the average percentage of products that were replaced in the Top 200 ranks for each product type of each website.
Amazon has clearly adopted a strategy of offering new options to its shoppers each day, with an average of 60% new products in the Top 200 ranks of the Fashion category. Contrast that with Walmart which appears to be more conservative in its approach to churning its Top 200 products. In the case of Neiman Marcus however, the reason for the lower volume of product pricing movements in its Top 200 ranks may be due to the relatively high value of its premium product assortment, which imposes the internal constraints of having a smaller pool of new products to choose from.
Online-First, This Black Friday Sale Season
Amazon continues to demonstrate its dominance as a pacesetter in US retail, largely due to its progressive online pricing and merchandising strategies. These embrace the power of big data in its approach to online retail.
Research shows online is consistently outperforming in-store along critical customer satisfaction dimensions spanning: product quality, selection and/or variety, availability of hard-to-find and unique products, ease of searching and delivery options.
According to global consultancy Deloitte, for the first time ever, American shoppers will purchase more online than they buy offline in the 2017 holiday shopping season — 51 percent, up from 47 percent in 2016. With Black Friday looming in the next few weeks, it will be interesting to see how US retailers push to seize a larger piece of this growing pie.
Check out our website to learn more about how DataWeave provides Competitive Intelligence as a Service to retailers and consumer brands globally.
As the battle of the Indian eCommerce heavyweights continues to accelerate, we have witnessed three separate sale events compressed into the last four weeks of this festive season. Flipkart has come out with all guns blazing following its multi-billion-dollar funding round, leaving Amazon with little choice but to follow suit with its own aggressive promotions. At this stage of a highly competitive eCommerce cycle, market share is a prize worth its weight in gold and neither Flipkart nor Amazon are prepared to blink first.
At DataWeave, our proprietary data aggregation and analysis platform enables us to seamlessly analyze these sale events, focusing on multiple dimensions, including website, category, sub-category, brand, prices, discounts, and more. Over the past six weeks, we have been consistently monitoring the prices of the top 200 ranked products spread over sub-categories spanning electronics, fashion, and furniture. In total, we amassed data on over 65,000 products during this period.
The first of these pivotal sale events was held between the 20th and 24th September, which we earlier analyzed in detail. Another major sale soon followed, contested by Amazon, Flipkart and Myntra for varying periods between the 4th and 9th of October. Lastly, was the Diwali season sale held by Amazon, Flipkart, and Myntra between the 14th and 18th of October, joined by Jabong between the 12th and 15th of October.
In analyzing these significant sale events for all eCommerce websites, we observed an extensive range of products enjoying high absolute discounts, but with no additional discounts during the sale, i.e. prices remained unchanged between the day before the sale and the first day of the sale. The following infographic highlights some of the sub-categories and products where this phenomenon was more pronounced during the recently concluded Diwali season sale. Here, discount percentages are average absolute discounts of products with unchanged discounts during the sale.
Having identified the aggressive use of high but unchanged absolute discounts amongst eCommerce heavyweights during the sale, we focused our analysis on the additional discounts offered during the sale, to more accurately reflect the value these sale events deliver to Indian consumers.
Several categories, sub-categories and brands emerged as enjoying substantial additional discounts. The following infographic details our analysis:
Amazon and Flipkart continue to stand toe to toe on discounts in Electronics, although Amazon offered discounts across a greater number of products. Flipkart adopted a more premium brand assortment in the Electronics category with an average MRP of INR 30,442 for additionally discounted products.
What stands out in our analysis is Amazon’s consistently aggressive discounting in fashion compared to Flipkart. As anticipated, Jabong and Myntra continued to offer attractive discounts in a large number of fashion products, seeking to maintain their grip in their niche. Furniture, too, is a category where Amazon out-discounted Flipkart, albeit through a less premium assortment mix (average MRP of INR 23,580 compared to Flipkart’s INR 34,304).
Several big brands elected to dig deep into their pockets during the sales to offer very high discounts. These included attractive discounts from Redmi, Asus, and Acer in Electronics, and W, Wrangler, Levi’s, Puma, Fossil, and Ray Ban in Fashion.
Which Sale Delivered Greater Value For Consumers?
Since DataWeave has extensive data on both the pre-Diwali sale (held between 4th and 9th of October), and the Diwali season sale (held between 12th and 18th October), we compared prices to identify which of the sale events offered more attractive discounts across categories, sub-categories and products.
While the discount levels were generally consistent across most sub-categories, only varying by a few percentage points, we identified several sub-categories and products that displayed a large variation in the absolute level of discount offered.
As the infographic above shows, Amazon identified women’s formal shoes as a key category in its discounting strategy, which saw its level of discounting triple during the Diwali sale. By comparison, Flipkart doubled its discount in men’s jeans, and Myntra tripled its discounts on Men’s shirts and sunglasses.
Similarly, during the Diwali sale Amazon, Flipkart and Myntra all offered selected products with an aggressive 40% to 50% discount level.
Interestingly, Amazon, Flipkart and Myntra all elected to reduce the level of discounts offered on specific products as well. One of the biggest discount moves was Amazon’s reduction on iPhone 6s from 34% to only 4%. Flipkart recorded a similar price move on Adidas originals Stan Smith sneakers (30% to 5%) and Canon EOS 200D DSLR cameras (20% to 8%).
Market Share Reigns Supreme
Based on our analysis of the festive season sales, Flipkart’s aggressive approach powered by its multi-billion-dollar funding round enabled it to stave off Amazon’s discounting strategy in the annual eCommerce festive season sales this year, increasing its lead over Amazon India in a market where the total sales is believed to have surged by up to 40 percent over 2016’s sales.
Based on several reports, Flipkart’s share of total festive season sales appears to have increased from 45 percent in 2016 to 50 percent this year, capturing much of the market up for grabs from a now relegated Snapdeal. Amazon’s market share during a festive sales period that stretched over a month is estimated to have remained steady at 35 percent, though the company reported it saw a 50 percent share in other metrics such as order volume and active customers.
The key question for both industry analysts and consumers alike is, how much deeper are retailers willing to go in their quest to capture market share at the expense of operating margins?
If you’re interested in DataWeave’s data aggregation and analysis platform, and how we provide Competitive Intelligence as a Service to retailers and brands, visit our website!
In the lead up to October’s Diwali celebrations, almost all major Indian e-retailers had announced mammoth sale events for last week. Resuming the epic battle of India’s online shopping carts during festival seasons, Flipkart, together with Jabong and Myntra, kicked off their five-day-long “Big Billion Days” sales on September 20, while Amazon India‘s “Great Indian Festival” launched the next day.
The stakes were high as Amazon and Flipkart are more evenly matched this year than ever before, making predicting an eventual winner of these dueling discounters a lot tougher than in previous years.
At DataWeave, our proprietary data aggregation and analysis platform enabled us to easily assess which e-retailer offered better deals and discounts. Over the last two weeks, we have been consistently monitoring the prices of the top 200 ranked products in Amazon, Flipkart, Myntra, and Jabong, across several sub-categories of Electronics, Men’s Fashion, and Women’s Fashion, encompassing over 35,000 products in total.
Divergent Discount Strategies
In our analysis, we bring focus to the additional discounts offered by competing e-retailers during the sale, compared to prices before the sale. This is key, as today’s shoppers often encounter deep discounts on several products even on normal days, which could potentially dampen the value suggested by the large discounts advertised during the sale.
Based on our analysis, Flipkart clearly adopted a more aggressive pricing strategy this year, establishing a lead over Amazon in average discount percentage for Electronics and Women’s Fashion. Moreover, Flipkart launched additional discounts on a larger number of products across categories. Amazon, though, offered 6.9 percent additional discounts on smartphones compared to Flipkart (6.2 percent), led by 10.7 percent discount on Apple and 7.7 percent discount on Redmi smartphones.
Flipkart has already reported a doubling of revenue from the sale (which includes sales volumes of Myntra and Jabong) compared to last year, and claimed it accounted for 70 percent of eCommerce sales during these five days — beating Amazon by a considerable margin. Amazon, for its part, reported a “2.5X growth in smartphone sales, 4X increase in large appliances and 7X in fashion sales.”
The difference in discounting strategies between Amazon and Flipkart is starkly illustrated by their respective highest discounts. Flipkart led the way with a 65.5 percent discount on Vero Moda skirts, a 65 percent discount on Tommy Hilfiger skirts, and 50 percent off Calvin Klein sunglasses.
By contrast, Amazon’s greatest discounts were an 83.4 percent discount on Redfoot formal shoes, 45.5 percent on Motorola Tablets, a 40 percent on US Polo T-shirts, and a 25.1 percent discount on Puma sports shoes.
Also, Flipkart hosted a more premium range of products in its assortment compared to Amazon, evidenced by a higher average MRP for its discounted products. Surprisingly, Amazon’s spread of discounted products has the least average MRP in Electronics and Women’s Fashion, compared to all other competitors.
New Products Break Through the Top 200
What’s fascinating in this battle of the e-retail giants is the correlation we uncovered between prices and rank. During the sale, as prices dropped on hundreds of products across the board, newer products successfully broke through into the Top 200 ranks for each sub-category. New products in the top 200 ranks had higher discount levels than the ones they replaced.
This trend was especially pronounced in fashion, where we observed an almost complete overhaul of products filling the Top 200 during the sale period, led by sports shoes in Amazon, Men’s shirts in Flipkart, and Men’s formal shirts in Jabong.
What About Pre-Sale Prices?
Another angle we explored was whether (like most of us suspect) e-retailers increase their prices before a sale, only to reduce them during the sale, so they can advertise higher discounts. We observed that all e-retailers did increase their prices for an albeit small set of products before the sale.
While the number of products where the prices increased for each website prior to the sales is small, it is interesting to observe that certain brands choose to perform the oldest trick in the retail book even today — raising prices to accentuate the degree of discount during the sale period, something shoppers need to keep an eye out for.
A Sign of Things to Come?
Based on our analysis, Flipkart has recognized the threat from Amazon and has approached this year’s “Big Billion Days” sale aggressively. It has dug deep into its freshly funded pockets, and offered better discounts for a larger set of products across most categories, in its attempt to lock down a greater market share in the burgeoning Indian eCommerce space.
Amazon, though, has continued to maintain a firm grip on the Indian consumer, having achieved tremendous growth in specific categories during the sale.
What’ll be interesting now is to see how these pricing strategies impact company revenues and margins, and how this will shape the soon-to-follow Diwali sales in mid-October.
If you’re intrigued by DataWeave’s data aggregation and analysis technology, and would like to learn more about how we help retailers and brands build and maintain a competitive edge, please visit our website.
Earlier this year, we witnessed Amazon and Walmart going head to head in a CPG goods price war of fluctuating intensity that soon rippled out to embrace the entire grocery industry.
This further intensified with Amazon’s takeover of Whole Foods and the Whole Foods’ subsequent announcement hinting at significant discounts toward the end of August.
Soon, Target announced it was lowering prices on literally “thousands of items.” As Mark Tritton, Target executive vice president and the chief merchandising officer put it, “We want our guests to feel a sense of satisfaction every time they shop at Target.”
To drive home the seriousness of their intent, Target nominated grocery staples such as cereal, paper towels, milk, eggs, baby formula, razors and bath tissue and vowed to, “eliminate more than two-thirds of their price.”
At DataWeave, we focused our proprietary data aggregation and analysis platform on Target’s reported price reduction. Our team acquired data on the prices of over 160,000 products listed by Target across 12 zip-codes selected at random. The platform then took two snapshots. Firstly, between 23rd August and 30th August which included the Whole Foods’ price reduction (to study any possible reactions on price) and, secondly, between the 6th September and 13th September, which included Target’s discount strategy announcement.
Of the categories Target identified as priorities for its discount strategy, only baby products, cereals, and Milk & Eggs displayed significant price drops. This price discounting effect varies, however, across brands in each category. In cereals, while KIND (30.4%) and Purely Elizabeth (24%) displayed high discounts, Apple Jacks, Corn Pops, and Krave more surprisingly increased their prices by up to 25% each.
Similarly, in the Milk & Eggs category, Price’s (13.6%) and Coffee-Mate (10%) exemplified hefty discounts, while Moon Cheese and Challenge Butter increased their prices by 33% and 48% respectively in the same time period. By comparison, Razors and Paper Towels showed no price changes whatsoever across the review period.
Interestingly, we observed greater price-change activity coinciding with the time of the Whole Foods’ announcement (between 23rd and 30th of August) than the later time period. Once again, however, no definite price discounting pattern emerged from the study, indeed the team found discount rates fluctuated significantly across categories.
Looking across the spectrum of CPG categories pricing, we saw significant, sustained variation across both categories and zip-codes.
Beauty products showed a 2 percent discount on average although this varied by zip-code, fluctuating between a 7 percent discount and an actual 10 percent price increase. F&B showed a 2 percent price increase, which jumped to 10 percent in some zip-codes. Personal care displayed a 2.5 percent increase on average, varying anywhere between an 8 percent discount and a 10 percent price increase. Baby products surprisingly recorded a 4 percent price increase on average during the study.
So, What Does This All Mean?
Based on our analysis, Target’s pricing strategy appears to be a combination of very closely concentrated discounting, complemented by selective price increases. Is discounting more a perception than a reality at this stage of the CPG cycle?
Aggressive price discounting has never been a decisive factor in successfully building Target’s consumer franchise. However, given the current trading environment and the continued pressure applied by competitive omni-channel strategies, which has seen a host of new entrants elbowing their way into the market, we anticipate price will continue to play a prominent role in retailing.
We suspect, based on evidence we gathered, that price discounts are more a highly targeted weapon in the fight for market share than a broadsword slashing of prices across the board. As Target’s CEO Brian Cornell noted during an earnings call, the company experienced “a meaningful increase in the percent of our business done at regular price and a meaningful decline in the percent on promotion.”
If you’re interested in DataWeave’s data aggregation and analysis technology, and would like to learn more about how we help retailers and brands build and maintain a competitive edge, visit our website.
Amazon.com, America’s retail behemoth, dominated headlines in August when it completed its acquisition of Whole Foods in early August 2017. Having officially taken control of the up-market grocer, which focuses on premium quality produce, market observers and consumers alike are eagerly awaiting Amazon’s pricing strategy analysis.
At the heart of Amazon.com’s seemingly unstoppable growth trajectory is the company’s ability to understand consumers, complemented by deep insights into buying cycles and purchase decisions and preferences. It also helps that Amazon.com boasts one of the planet’s mightiest marketing and publicity machines.
Is Amazon.com About To Launch A Grocery Price War?
Reports of Amazon.com dropping Whole Foods prices by up to 43 percent quickly made splashes across the news media. Given Jeff Bezos has been quoted in the past as saying, “your margin is our opportunity”, an aggressive promotional campaign to achieve dominance for its new Whole Foods acquisition was anticipated by some commentators.
These sentiments ignited fears of a profit-sapping price war, immediately hit stock prices in the cutthroat grocery industry, which survives on famously thin margins. Memories of Amazon.com’s impact on US department store profitability quickly surfaced with analysts pointing to Walmart’s revenue/market share plunge from 26 percent in 2005 to just 11 percent in 2016 when the sector came under sustained pressure from Amazon.com.
How Deep Are Amazon.com’s Price Cuts Really?
At DataWeave, a Competitive Intelligence as a Service provider for retailers and brands, we put Amazon.com’s actual Whole Foods discounts under the microscope. The resulting careful analysis of price discounts revealed quite a different story to the one initially featured in the media. Scrutiny by our proprietary data aggregation and analysis platform showed the drop in retail grocery prices was minimal to almost negligible, depending on the category.
In delivering near-real-time competitive insights to retailers and brands, we acquire and compile large volumes of data from the Web on an ongoing basis. A key differentiator is our ability to aggregate data down to a zip-code level.
Our analysis of Amazon.com’s reported drop in prices was based on data acquired for 13 zip-codes distributed across the country and selected at random. Our platform compared market prices by zip code valid between 23rd August and 30th August.
Each zip code indicated the overall average discount offered varied between 0.20 percent and -0.20 percent. When the discounts at a category-level were separated out, the discounts available to customers per category varied between -6.8 percent (an actual price increase) and 6.1 percent.
Moving on to the “Fill the Grill” category, discounts again were modest, varying between -5.6 percent (another price increase) and 6.1 percent across the zip codes analyzed.
This aligns with Amazon.com’s recognized preference for basing its strategy on competing on breadth and depth of product assortment rather than pure pricing discounts at the checkout.
Some Sunshine For Foodies
There was some good news for shoppers looking for higher discounts. Amongst those products attracting a higher discount were:
Belton Farm Oak Smoked Cheddar Cheese: 50 percent
Beemster Premium Dutch Cheese: 50 percent
Heritage Store Black Castor Oil: 50 percent
Organic French Lentils: 45 percent
Vibrant Health Pro Matcha Protein: 40 percent
Hass Avocado: 50 percent (confined to one zip-code).
Final Word
Amazon.com’s marketing engine is renowned for skillfully nurturing consumer price perceptions of the giant retail website as being the lowest priced retailer. We kept a keen eye on Amazon’s pricing these past weeks, and unearthed a carefully conceived and executed Whole Foods pricing campaign, which is yet another example of their market shaping expertise at work.
If you’re intrigued by DataWeave’s technology and would like to learn more about how we help retailers and brands build and maintain a competitive edge, please visit our website!
When retailers today look to compete in the cutthroat world of online commerce, they face several challenges unique to the nature of modern retail. It is now significantly harder for retailers to benchmark their pricing, assortment, and promotions against their competition, as the online world is highly dynamic and significantly more complex than before.
Trends like the growing adoption of mobile shopping apps, the rising influence of customer reviews in buying behavior, hyperlocal e-commerce websites differentiating themselves by fulfilling deliveries in a matter of hours — the list goes on — have only added to this complexity.
However, this complexity also presents an opportunity for retailers to incorporate layers of external competitive information into their merchandising strategies to deliver more value to customers and personalize their experience.
Vipul Mathur, Chief Branding and Merchandising Officer at Aditya Birla Online Fashion, recently published an article highlighting some of the areas in which Competitive Intelligence providers like DataWeave can strategically influence modern merchandising.
“The consumer is often driven by the aesthetics of a product, more so in the fashion and lifestyle industries than others. Hence, the choices of buyers are hard to interpret. However, innovative modern technologies are helping us understand these decisions,” says Vipul.
He provides an example of how using AI-based tools (like DataWeave’s) to unearth the sentiments behind thousands of online reviews can help retailers better channel and message their online promotions.
“Deciphering the consumers’ comments and converting them into tangible insights is incredible proof of the refinement possible with data analysis tools. It’s like knowing that consumers are delighted by the quality of the soles of a pair of Adidas running shoes. Using this, marketing communication can be modified to highlight this specific product feature,” explains Vipul.
And it’s not just merchandising. This data can percolate across multiple functions in retail, enabling greater efficiency in operations. “If we have data on the best-selling styles across websites, including other attributes like pricing, region/locality (through pin-code mapping), and possibly even rate of sales, it’s up to our supply-chain systems to ensure that the supply is in accordance with demand.”
DataWeave’s Retail Intelligence offers global retailers and e-commerce websites with these benefits and more. Our AI-powered technology platform aggregates and analyzes vast volumes of online competitive data and presents them in an easily consumable and actionable form, aiding quick, data-driven merchandising decisions.
“DataWeave, our partner, has helped us refine our merchandising decisions, saving cost and creating value,” sums up Vipul.
Read the entire article here, and if you’re intrigued by what DataWeave can do for retail businesses and wish to learn more, visit our website!
Images, intrinsically, are complex forms of information, with varying backgrounds, orientations, and noise. Developing a reliable system that achieves human-like accuracy in identifying, interpreting, and comparing images, without investing in expensive resources, is no mean task.
For DataWeave, however, the ability to accurately match images is fundamental to the value we provide to retailers and consumer brands.
Why Match Images?
Our customers rely on us for timely and actionable insights on their competitors’ pricing, assortment, promotions, etc. compared to their own. To enable this, we need to identify and match products across multiple websites, at very large scale.
One might hope to easily match products using just the product titles and descriptions on websites. However, therein lies the rub. Text-based fields are typically unstructured, and lack consistency or standardization across websites (especially for fashion products). In the following example, the same Adidas jacket is listed as “Tiro Warm-Up Jacket, Big Boys (8–20)” on Macy’s and “Youth Soccer Tiro 15 Training Jacket” on Amazon.
Hence, instead of using text-based information, we considered using deep-learning techniques to match the images of products listed on e-commerce websites. This, though, requires massive GPU resources and training data fed into the deep-learning model — an expensive proposition.
The solution we arrived upon, was to complement our image-matching system with the text-based information available in product titles and descriptions. Analyzing this combination of both text- and image-based information enabled us to efficiently match products at greater than 90% accuracy.
How We Did It
A couple of weeks ago, I gave a talk at Fifth Elephant, one of India’s renowned data science conferences. In the talk, I demonstrated DataWeave’s innovation of augmenting the NLP capabilities of Solr (a popular text search engine) with deep-learning features to match images with high accuracy.
Check out the video of the presentation for a detailed account of the system we built:
Human-Aided Machine Intelligence
All products matched with the seed product are tagged with a corresponding confidence score. When this score crosses a certain threshold, it’s presumed to be a direct match. The ones that are part of a lower range of confidence scores are quickly examined manually for possible direct matches.
The outcome, therefore, is that our technology narrows down the consideration set of possible product matches from a theoretical upper limit of millions of products, to only a few tens of products, which are then manually checked. This unique approach has two distinct advantages:
The human-in-the-loop enables us to achieve greater than 90% accuracy in matching millions of products — a key differentiator.
Information on all manually matched products is continually fed to the deep-learning model, which is used as training data, further enhancing the accuracy of the product matching mechanism. As a result, both our accuracy and delivery time keep improving with time.
As the world of online commerce continues to evolve and becomes more competitive, retailers and consumer brands need the ability to make quick proactive and reactive decisions, if they are to stay competitive. By building an automated self-improving system that matches products quickly and accurately, DataWeave enables just that.
Find out more about how retailers and consumer brands use DataWeave to better understand their competitive environment, optimize customer experience, and drive profitable growth.
Amazon’s Prime Day sale, the first-of-its-kind in India, made a conspicuous splash across the media a couple of weeks ago, with several stories of the sale’s dramatic success doing the rounds. For 30 hours spread over 10th and 11th of July, the online retail giant rolled out deals as frequently as every five minutes, exclusively for Amazon Prime subscribers. And online shoppers lapped it up.
According to Amazon India, more customers signed up for Prime on the day of the sale and in the week leading up to it, than on any other month since Prime’s launch in India last year. To boot, Prime subscribers shopped three-times more during the sale compared to other days.
The discounts offered on several products were quite frequently in the range of 60–70% and beyond, with some products reaching absurd discount levels of up to 85%. However, for a retailer as competitively priced as Amazon, what’s interesting to explore is how much additional discount was offered during the sale. After all, even on normal days, Amazon discounts aggressively on its top 20% selling SKUs, in order to reinforce the commonly held perception that the company is the lowest priced retailer around.
More Than Meets the Eye
At DataWeave, our AI-based technology platform aggregates and analyzes publicly accessible data on the Web, at large scale, to deliver insights on competitors to retailers and consumer brands. We collected pricing and discount information for the Electronics and Fashion categories on Amazon during the sale, and compared it to numbers from before the sale. Thus, we evaluated just how much additional value Prime subscribers could’ve potentially drawn from this sale.
We performed a similar analysis on Flipkart as well, to examine how competing e-commerce websites react to big-ticket sale events.
The infographic below lists out some of the more interesting bits of our analysis.
Unsurprisingly, Amazon strengthened its grip in the electronics category by offering, on average, 3.9% higher discount than Flipkart, even with a higher-value assortment mix. Subsequently, Amazon reported a 5X increase in sales of smartphones and an 8X increase in sales of televisions during Prime Day.
While Apple discounted its phones by 8.5% during the sale, Sanyo was among the top discounting brands (10%) in Televisions, with the company reporting a 4X jump in television sales. TCL offered 20% additional discount, the highest for televisions.
What stands out from this analysis, though, is that Flipkart beat Amazon on price definitively in the fashion for women category, by extending 6.8% more discount than Amazon on a significantly higher-value assortment mix.
It’s not uncommon to see e-commerce companies lowering their prices across the board to take advantage of the hype surrounding a competing e-commerce website’s promotional activity. Clearly, it’s a good idea for shoppers to always compare prices across websites before buying any product online.
The New Age of Retail
That shoppers today can easily compare products and prices across different e-commerce websites has brought about greater competition among online retailers. With the consequent margin pressure, comes the need for retailers to be able to react to price changes by their competitors in near-real-time.
And it’s no mean task. Amazon has been found to effect over 80 million price changes a day during holiday season, and retailer-driven sale events like the Prime Day Sale are here to stay. Consequently, retailers look to Competitive Intelligence providers like DataWeave for easily consumable competitive information that enables them to react effectively and compete profitably.
DataWeave’s AI-powered technology platform aggregates, compiles, and presents millions of data points to provide e-commerce companies with actionable competitive insights. With our solutions, retailers can effect profitable price changes, implement high-value assortment expansion, and proactively monitor and respond to promotional campaigns by competitors.
Find what we do interesting? Visit our website to find out more about how modern retailers benefit from using DataWeave’s Competitive Intelligence as a Service.
For years, the consumer electronics chain Radioshack has endeavored to stay alive in our ever-changing world. Despite their efforts, they have filed for bankruptcy for the second time, in as many years. As of now, the company is closing 200 of their 1,500 stores, slightly more than 13% of their locations
This one-time retail “giant” isn’t alone on the path of reduction in force. Macy’s has announced that they will close 63 stores, and Sears will lock their doors for the final time on 150 of their stores this fiscal year.
Brands too are feeling the heat. Ralph Lauren recently announced the closure of an unspecified number of stores (including its Polo store on Fifth Avenue, New York City), and a reduction in its workforce.
The internet is impacting brick and mortar sales the way that Sears Roebuck and Montgomery Ward catalog mail order sales impacted the general store at the turn of the last century.
Online Retail Plays the Spoiler
The disruption of the retail industry following the onset of e-commerce is largely due to the change in shopping behavior. Shoppers today can sit at home and compare multiple retailers before making a purchase. This has a significant impact on consumer expectations and how retailers do business today.
Smartphone apps make comparing prices, and downloading coupons simple. So, we now see e-retailers compete tooth-and-nail on price, and even willing to take the “loss leader” route to drive adoption. Consequently, consumers expect rock bottom prices. Many brick-and-mortar retailers like Walmart have responded by simply matching online prices.
While there are tens of thousands of e-commerce companies in the world today, this disruption is led primarily by the behemoth of global retail — Amazon.
The Torchbearer of Modern Retail
Amazon’s retail business strategy rests on three pillars: price perception, broad assortments, and customer experience.
Price has long been the primary driving factor in retail. Therefore, there is need to optimize price efficiently to drive revenue and margins. What Amazon has smartly done is to drive the perception among shoppers that the company is always the lowest priced, even though it’s untrue. They do this by ensuring they are the lowest priced in the top 20% selling SKUs by volume. The resulting perception among consumers is a key differentiator.
Also, to deliver superior customer experience compared to competing retailers, Amazon ensures high quality of online catalogs, provides a wide selection of products, and offers fast shipping to a broad coverage area, at no additional cost.
When you factor in the Amazon Prime service, consumers have become spoiled with receiving their purchases within 48 hours. Sunday deliveries, and scheduling within the hour means buyers are in the driving seat.
Some of Amazon’s competitors are following suit. Mega box stores like Costco, in an endeavor to meet their customers’ desire for options, are partnering with Google Express to provide fast delivery of household items, apparel, electronics, pantry staples such as bread and cereal, and more.
The message is clear — today’s brick-and-mortar retailers need to have an omni-channel approach to retail, and an online presence if they are to stay competitive and relevant. However, this move has its fair share of obstacles –
The Challenge of Moving Online
Brick-and-mortar retailers moving online are confronted with several questions that carry more weight today than they used to in the past:
How do I deliver a high-quality shopping experience?
How can I drive price perception among shoppers?
What products do I promote and when?
What product assortment do I build to drive sales and retention?
How do I manage my logistics to reduce shipping cost and time?
Traditional retailers looked largely at only internal data — like POS data, product sell-through rates, inventory, etc. to answer these questions. Today, it is mission-critical for retailers to absorb and utilize external competitive data as well — and here lies the problem. When you are benchmarking yourself against the competition online, it is that much harder, as it’s more dynamic and significantly more complex than before.
For example, Forbes estimated that through Christmas season in 2014, Amazon made a total of 80 million price changes per day to stay competitive. These are extraordinary numbers, and reflect how dynamic online retail is, and its contrast to traditional retail.
Retailers today have no choice but to automate as much as possible, so they can make quick, timely merchandising decisions and keep pace with modern e-retail. Retail technology providers like DataWeave have stepped in to meet this demand.
DataWeave’s Retail Intelligence
At DataWeave, we enable retailers gain a competitive advantage in the online world by providing Competitive Intelligence as a Service. We do this by harnessing public information on the competition, structuring it, and presenting it in a form that is easily consumable and actionable, enabling easy, automated decision-making.
Our AI-based technology platform facilitates smarter pricing decisions by providing retailers with price change (increase and decrease) opportunities as they occur. Retailers can also plug gaps in their product portfolio by identifying opportunities to expand their assortments. In addition, they can benchmark their shipping speed and cost against competition, to enhance customer experience. And there’s more where these come from!
Click here to find out more about how we can help modern retailers stay competitive in the online world.
We all know, online shopping offers a lot of benefits to shoppers. Apart from the convenience it offers access to a wide-assortment base and, of course, discounts are an added benefit. Often we see, retailers claiming large discounts on products.
Many-a-time, the percentage discount that is mentioned drives price perception. Customers when comparing prices across stores view larger percentage discounts as a better deal. However, this is not necessarily the case. To present this case, let us look into how discounts are calculated:
Percentage discounts are a function of the MRP / MSRP and the Selling Price. The MRP / MSRP is set by the manufacturer and the selling price is more often than not determined by the retailer.
Selling price of products being different across retailers is a well-known fact. When the MRP of the same products tend to vary across retailers, it gets confusing for a customer, which in turn leads to a brand equity dilution of the brand or manufacturer.
To analyse how deep this discord is, we decided to dive deeper into its working dynamics. Amongst all the data that we aggregate at DataWeave, analysing discounts of the same product across retailers gives us the ability to discern pricing strategies of retailers. We used this dataset to monitor and analyse MRPs.
What we found
1. We analysed MRPs of around 400 brands across 10 categories. Around 44% of products in these brands have no variance in MRPs across retailers
2. This also means there is a variance in 56% of products
3. Products in the ‘Mobile Phones and Tablets’ category have the most price variance; 65% of the products have price variance
4. Fashion and Fashion accessories have the least price variance; around 20%
5. Brands having the most variance:
6. Brands having the least variance:
What are the implications of the above insights?
Brands & manufacturers need to be aware of how their brand products are being represented and sold online
Consumers shopping online need to look at end prices, and not focus on the discount percentage, before making a purchase-decision on a particular store
This article was previously published on Yourstory
DataWeaves Brand Intelligence provides consumer brands with the ability to track their products, pricing, discoverability vis-a-vis their competitors across e-commerce platforms.
After a thorough assessment of our product FinancesOnline, a well-known software review platform and SaaS leads generation source, awarded DataWeave Retail Intelligence with two of their prestigious industry awards. According to FinancesOnline, our specialized competitive intelligence product is a rare tool that handles different languages with ease, and it allows businesses to improve the margin of their products and be more competitive.
Currently, DataWeave Retail Intelligence holds two of the platform’s prominent awards: the 2016 Great User Experience Award given to products which facilitate complex operations and allow users to navigate an easy and familiar interface; and the 2016 Expert’s Choice Award, confirming that DataWeave employs a variety of unique mechanisms to produce valuable competitors’ insights, compares and measures metrics that matter to every online store. Both awards were given for the platform’s business intelligence software reviews category.
According to their DataWeave review here the experts believe DataWeave genuinely focused on making businesses more competitive instead of simply listing data that may not be actionable by the company. They were particularly fond of the advanced identification of weak and strong points, actionable insights, and assortment intelligence, but mentioned as well the positive aspects of combining internal analytics with market data the way DataWeave does it. They praised our efforts to surpass traditional functionality gaps arising from language and location restrictions, and seem to firmly believe that out well-planned integrations make DataWeave usable for all type of analysis. Continuing with this tempo, FinancesOnline’s B2B professional foresee DataWeave performing successfully in many areas other than retail.
“When it comes to retail markets, law of one price is no law at all” — Hal Varian
Hal Varian, in his seminal paper “A Model of Sales”, further remarks that most retail markets are instead characterized by a rather large degree of price dispersion.
Do you know how much your products are worth? How low are you willing to price an item to compete with another ecommerce retailer?
Today, online retail has become increasingly competitive. If you are priced higher than your competitors, you may end up losing customers who are sensitive to prices. With the advent of highly competitive pricing tools, winning the online pricing war is an uphill task. Having a differentiated competitive strategy is critical to your e-commerce success.
We bring to you a list of smart practices that we have seen being played out across online retailers in 10 countries that we actively monitor and analyze.
Analyzing Competitor Prices And Stock Availability
Product pricing is one of the largest driver of profitability. So you know who your main competitors are, but do you know how they are priced? Compare prices and stock availability of products that are popular across all your competitors and do the same for products that are popular at your store. If you know that certain products are “not in stock”, you know you need not discount. Look at products that are popular across competition and know your price position. Try for an opportunity to increase prices without losing your price position. However, for products popular on your store, you may want to stay competitive.
Knowing Price Variations
You get the right price, and then it’s not right anymore. That’s the story of online retail. But when you are equipped with the knowledge of price variations on popular marketplaces, it gives you an idea of where the market is heading. This, in turn, will help you adjust your prices to get the consumers. For instance, Amazon changed prices of more than 50% of their products in Hair Care category more than once in a week including ~20% of the products at least 4 times in the same week.
Product Bundling
A marketer of a successful product may bundle a new or less successful product with its stronger product to edge its way into a new market. This allows you to charge a unique, competitive price that can’t be copied by others. If you realize that you may not be able to compete on direct discounts, bundle products together and offer them at a lower price. You can either bundle in multiples of the same product or pack different products together. One of the more famous examples of this is Microsoft’s bundling of various software applications. In the onsite retail space, for example, on a particular day we noticed ~400+ combo offers from SnapDeal in the camera & accessories category whereas PayTM has ~200+ combo offers and Amazon has ~3000+ combo offers in the same category. Similarly, in hair care category we observed significant variance in combo offers across marketplaces (~900+ by Amazon, ~250+ by PayTM and ~100 by SnapDeal on a specific day). We also noticed that marketplaces have varied number of products sold in packs across different brands (~2500 in Amazon, ~800+ in PayTM and ~500 in Snapdeal on a specific day).
Shipping Fees & Delivery Time
Free shipping attracts customers to e-commerce platforms like a moth to a flame. Monitor shipping fees across competition for products you are interested in. There will be cases where your competitor is pricing a product at a lower price than you, but does not offer free shipping. That is your signal to promote your platform.
Price Match Guarantees
Price match is an easy way for customers to save money on their day-to-day purchases. During Black Friday sales in the US, a lot of popular stores go for the price match guarantee feature to drive sales. It’s a smart trick to let your customers show you the lowest price and then match them accordingly.
No Discounts On Unique Products
No matter how much you dress it up, cutting prices hurts. It might be unavoidable, but you can get rid of discounts on unique products. When you analyze gaps and strengths of your catalog and realize that there are products that are available only on your store, why would you need to provide discounts? So, for instance, it seems that only Flipkart is carrying Icon LaserJet Pro Black Toner currently and it is being sold at 75% discount. Unless the objective is to get rid of the inventory, this product could be priced higher. Another example is, Nikon Coolpix S1100PJ Point & Shoot Camera is out of stock with most of the key marketplaces. Hence if anyone gets this replenished, this should not be discounted. Similarly, unique brands in hair care category, say LeModish, is sold primarily on PayTM. So, PayTM could look at reducing discount for this brand.
Don’t Price Above Market Rate
Some retailers price products above the market rate (MRP / MSRP) so that they can show substantial discounts. But your customers are smart and research well. If they realize that this is not really ‘low price’, you may end up losing them.
Dynamic Pricing
This is one trend you should definitely follow. Constantly monitor competitor prices and drop or increase prices whenever you see an opportunity. This process is highly tech-driven, so ensure that you work with a vendor who provides the same or you have the in-house capability to do this in a sustained and scalable manner.
There are multiple product strategies that have to be considered, including cross-border commerce and highly spread out markets like SEA where there exists a lot more C2C marketplaces. However, as with many things in ecommerce, one size does not fit all. Combine the powers of your service and price to drive your bottom line and emerge as an undisputed leader in the retail space.
DataWeave Retail Intelligence provides competitive intelligence solution to retailers. DataWeave’s solution is both language and geography agnostic and is built for significant scale
PriceWeave provides Competive Intelligence for eRetailers, brands, and manufacturers. Competitive Intelligence helps businesses understand their competition better, take timely decisions, and increase sales. Our retail pricing intelligence tool serves the following major purposes:
Compare: PriceWeave lets you access products from across any number of sources and organize them for a straightforward apples-to-apples comparison.
Monitor: Our intuitive dashboards help you monitor prices, assortments, products, brands, and deals across competition on a daily basis.
Discover: Discover gaps in your product catalog. Discover products that are unique to you. Discover new brands and categories your competitors have introduced. Find new competitors.
Analyze: Get customized alerts and reports on anything that you want to track. Access historical pricing data to understand pricing strategies. Visualize data across facets at different levels of granularity.
If you are an eRetailer, PriceWeave powers your sales, marketing, and analytics team with actionable data–for both day to day operations, as well as long term strategy. With retail pricing intelligence, an eRetailer can:
understand pricing opportunitiesand implement an effective pricing strategy
get pricing variation for the products you are tracking across competition
get apples-to-apples product comparison and historical pricing data
In the aggressive business of online retail every detail you know about your competitor gives you an edge over them. To help you stay ahead of your competition we have designed a series of blog posts that familiarize you with competitive intelligence and equip you to get maximum mileage out of competitive intelligence tools. This is the first post of the series.
Let’s begin at the beginning.
What is Competitive Intelligence?
Competitive intelligence (CI) is the gathering of publicly-available information about an enterprise’s competitors and the use of that information to gain a business advantage.
Competitive marketing intelligence helps managers and executives to make data-driven decisions both in the short term, as well as formulate medium to long term strategy.
Why is Competitive Intelligence important?
Competitive marketing intelligence is critical because it helps businesses stay ahead of the competition by:
Augmenting one’s experience and instincts with hard data and analyses on a regular basis
Delivering reasonable assessments of one’s own business vis-a-vis competitors’ businesses
Identifying and alerting new business opportunities as well as threats
Helping shape short term and long term strategies to grow and consolidate one’s business
How does Competitive Intelligence help achieve the core objectives of retail business?
Retail is a particularly competitive sector. Given the volume of transactions that happen in the retail sector, even a slight improvement in metrics has a huge impact. Thus, competitive Intelligence has a direct effect on the bottom line. It helps in the following ways:
> Improve margins
This is a result of optimized pricing of products. Knowing the competitors pricing goes a long way in pricing your products right and improving margins. With Competitive Intelligence on your side, you can take pricing decisions backed by data.
> Reduce customer acquisition costs
By improving your assortment mix more users looking for products that your site offers become your users. This helps reduce customer acquisition costs. This also helps in retaining existing customers
> Optimize marketing spend
Competitive Intelligence brings more clarity and sharper objectives for the marketing team. You get good indicators which products/categories your competitors are promoting, and which new brands/categories they have introduced. This helps streamline and optimize your market spend.
This is where DataWeave comes in. DataWeave provides Competitive Intelligence for retailers, brands, and manufacturers. DataWeave is built on top of huge amounts of product data to provide features such as: pricing opportunities (and changes), assortment intelligence, gaps in catalogs, reporting and analytics, and tracking promotions, and product launches.
DataWeave is powered by distributed data crawling and processing engines that enables serving millions of data points around products data refreshed on a daily basis. This data is presented through dashboards, notifications, and reports. PriceWeave brings the ability to use BigData in compelling ways to retailers.
DataWeave lets you track any number of products across any categories against your competitors. If you wish to try this out, just book a free discovery call with us.
In the next few posts, we will dig deeper into DataWeave and introduce its major features. We will also talk about how each of these features help you in improving your business metrics.
We have seen a steady increase in the number of smartphones and tablets since the last five years. Looking at the number of smartphones, tablets and now wearables ( smart watches and fitbits ) that are being launched in the mobiles market, we can truly call this ‘The Mobile Age’.
We, at DataWeave, deal with millions of data points related to products which vary from electronics to apparel. One of the main challenges we encounter while dealing with this data is the amount of noise and variation present for the same products across different stores.
One particular problem we have been facing recently is detecting whether a particular product is a mobile phone (smartphone) or a tablet. If it is mentioned explicitly somewhere in the product information or metadata, we can sit back and let our backend engines do the necessary work of classification and clustering. Unfortunately, with the data we extract and aggregate from the Web, chances of finding this ontological information is quite slim.
To address the above problem, we decided to take two approaches.
Try to extract this information from the product metadata
Try to get a list of smartphones and tablets from well known sites and use this information to augment the training of our backend engine
Here we will talk mainly about the second approach since it is more challenging and engaging than the former. To start with, we needed some data specific to phone models, brands, sizes, dimensions, resolutions and everything else related to the device specifications. For this, we relied on a popular mobiles/tablets product information aggregation site. We crawled, extracted and aggregated this information and stored it as a JSON dump. Each device is represented as a JSON document like the sample shown below.
From the above document, it is clear that there are a lot of attributes that can be assigned to a mobile device. However, we would not need all of them for building our simple algorithm for labeling smartphones and tablets. I had decided to use the device screen size for separating out smartphones vs tablets, but I decided to take some suggestions from our team. After sitting down and taking a long, hard look at our dataset, Mandar had an idea of using the device dimensions also for achieving the same goal!
Finally, the attributes that we decided to use were,
Size
Title
Brand
Device dimensions
Screen sizeI wrote some regular expressions for extracting out the features related to the device screen size and resolution. Getting the resolution was easy, which was achieved with the following Python code snippet. There were a couple of NA values but we didn’t go out of our way to get the data by searching on the web because resolution varies a lot and is not a key attribute for determining if a device is a phone or a tablet.
size_str = repr(doc["Display"]["Size"]) resolution_pattern = re.compile(r'(?:\S+\s)x\s(?:\S+\s)\s?pixels') if resolution_pattern.findall(size_str): resolution = ''.join([token.replace("'","") for token in resolution_pattern.findall(size_str)[0].split()[0:3]]) else: resolution = 'NA'
But the real problems started when I wrote regular expressions for extracting the screen size. I started off with analyzing the dataset and it seemed that screen size was mentioned in inches so I wrote the following regular expression for getting screen size.
However, I noticed that I was getting a lot of ‘NA’ values for many devices. On looking up the same devices online, I noticed there were three distinct patterns with regards to screen size. They are,
Screen size in ‘inches’
Screen size in ‘lines’
Screen size in ‘chars’ or ‘characters’
Now, some of you might be wondering what on earth do ‘lines’ and ‘chars’ mean and how do they measure screen size. On digging it up, I found that basically both of them mean the same thing but in different formats. If we have ‘n lines’ as the screen size, it means, the screen can display at most ‘n’ lines of text at any instance of time. Likewise, if we have ‘n x m chars’ as the screen size, it means the device can diaplay ‘n’ lines of text at any instance of time with each line having a maximum of ‘m’ characters. The picture below will make things more clear. It represents a screen of 4 lines or 4 x 20 chars.
Thus, the earlier logic for extracting screen size had to be modified and we used the following code snippet. We had to take care of multiple cases in our regexes, because the data did not have a consistent format.
Thus, the earlier logic for extracting screen size had to be modified and we used the following code snippet. We had to take care of multiple cases in our regexes, because the data did not have a consistent format.
Mandar helped me out with extracting the ‘dimensions’ attribute from the dataset and performing some transformations on it to get the total volume of the phone. It was achieved using the following code snippet.
dimensions = doc['Body']['Dimensions'] dimensions = re.sub (r'[^\s*\w*.-]', '', dimensions.split ('(') [0].split (',') [0].split ('mm') [0]).strip ('-').strip ('x') if not dimensions: dimensions = 'NA' total_area = 'NA' else: if 'cc' in dimensions: total_area = dimensions.split ('cc') [0] else: total_area = reduce (operator.mul, [float (float (elem.split ('-') [0])/10) for elem in dimensions.split ('x')], 1) total_area = round(float(total_area),3)
We used PrettyTable to output the results in a clear and concise format.
Next, we stored the above data in a csv file and used Pandas, Matplotlib, Seaborn and IPython to do some quick exploratory data analysis and visualizations. The following depicts the top ten brands with the most number of mobile devices as per the dataset.
Then, we looked at the device area frequency for each brand using boxplots as depicted below. Based on the plot, it is quite evident that almost all the plots are right skewed, with a majority of the distribution of device dimensions (total area) falling in the range [0,150]. There are some notable exceptions like ‘Apple’ where the skew is considerably less than the general trend. On slicing the data for the brand ‘Apple’, we noticed that this was because devices from ‘Apple’ have an almost equal distribution based on the number of smartphones and tablets, leading to the distribution being almost normal.
Based on similar experiments, we noticed that tablets had larger dimensions as compared to mobile phones, and screen sizes followed that same trend. We made some quick plots with respect to the device areas as shown below.
Now, take a look at the above plots again. The second plot shows the distribution of device areas in a kernel density plot. This distribution resembles a Gaussian distribution but with a right skew. [Mandar reckons that it actually resembles a Logistic distribution, but who’s splitting hairs, eh? ;)] The histogram plot depicts the same, except here we see the frequency of devices vs the device areas. Looking at it closely, Mandar said that the bell shaped curve had the maximum number of devices and those must be all the smartphones, while the long thin tail on the right side must indicate tablets. So we set a cutoff of 160 cubic centimeters for distinguishing between phones and tablets.
We also decided to calculate the correlation between ‘Total Area’ and ‘Screen Size’ because as one might guess, devices with larger area have large screen sizes. So we transformed the screen sizes from textual to numeric format based on some processing, and calculated the correlation between them which came to be around 0.73 or 73%
We did get a high correlation between Screen Size and Device Area. However, I still wanted to investigate why we didn’t get a score close to 90%. On doing some data digging, I noticed an interesting pattern.
After looking at the above results, what came to our minds immediately was: why do phones with such small screen sizes have such big dimensions? We soon realized that these devices were either “feature phones” of yore or smartphones with a physical keypad!
Thus, we used screen sizes in conjunction with dimensions for labeling our devices. After a long discussion, we decided to use the following logic for labeling smartphones and tablets.
device_class = None if total_area >= 160.0: device_class = 'Tablet' elif total_area < 160.0: device_class = 'Phone' if 'lines' in screen_size: device_class = 'Phone' elif 'inches' in screen_size: if float(screen_size.split()[0]) < 6.0: device_class = 'Phone'
After all this fun and frolic with data analysis, we were able to label handheld devices correctly, just like we wanted it!
Product Matching is a combination of algorithmic and manual techniques to recognize and match identical products from different sources. Product matching is at the core of competitive intelligence for retail. A competitive intelligence product is most useful when it can accurately match products of a wide range of categories in a timely manner, and at scale.
Shown below is PriceWeave’s Products Tracking Interface, one of the features where product matching is in action. The Products Tracking Interface lets a brand or a retailer track their products and monitor prices, availability offers, discounts, variants, and SLAs on a daily (or a more frequent) basis.
A snapshot of products tracked for a large online mass merchant
Expanded view for a product shows the prices related data points from competing stores
Product Matching helps a retailer or a brand in several ways:
Tracking competitor prices and stock availability
Organizing seller listings on a marketplace platform
Discovering gaps in product catalog
Filling the missing attributes in product catalog information
Comparing product life cycles across competitors
Given its criticality, every competitive intelligence product strives hard to make its product matching accurate and comprehensive. It is a hard problem, and one that cannot be complete addressed in an automated fashion. In the rest of this post, we will talk about why product matching is hard.
Product Matching Guidelines
Amazon provides a guideline to sellers about how they should write product catalog information in order to achieve a good product matching with respect to their seller listings. These guidelines apply to any retail store or marketplace platform. The trouble is, more often than not these guidelines are not followed, or cannot by retailers because they don’t have access to all the product related information. Some of the challenges are:
Products either don’t have a UPC code or it is not available. There are also non-standard products, unbranded products, and private label products.
There are products with slights variations in technical specifications, but the complete specs are not available.
Retailers manage a huge catalog of accessories, for instance Electronics Accessories (screen guards, flip covers, fancy USB drives, etc.).
Apparels and Lifestyle products often have very little by way of unique identifiers. There is no standard nomenclature for colors, material and style.
Products are often bundled with accessories or other related products. There are no standard ways of doing product bundling.
In the absence of standard ways of representing products, every retailer uses their own internal product IDs, product descriptions, and attribute names.
Algorithmic Product Matching using “Document Clustering”
Algorithmic product matching is done using some Machine Learning, typically techniques from Document Clustering. A document is a text document or a web page, or a set of terms that usually occur within a “context”. Document clustering is the process of bringing together (forming clusters of) similar documents, and separating our dissimilar ones. There are many ways of defining similarity of documents that we will not delve into in this post. Documents have “features” that act as “identifiers” that help an algorithm cluster them.
A document in our case is a product description — essentially a set of data points or attributes we have extracted from a product page. These attributes include: title, brand, category, price, and other specs. Therefore, these are the attributes that help us cluster together similar products and match products. The quality of clustering — that is how accurate and how complete the clusters are — depends on how good the features are. In our case, most of the times the features are not good, and that is what makes clustering, and in turn product matching, a hard problem.
Noisy Small Factually Weak (NSFW) Documents
The documents that we deal with, the product descriptions, are not well formed and so not readily usable for product matching. We at PriceWeave characterize them endearignly as Noisy Weak and Factually Weak (NSFW) documents. Let us see some examples to understand these terms.
Noisy
Spelling errors, non-standard and/or incomplete representations of product features.
Brands written as “UCB” and “WD” instead of “United Colors of Benetton” and “Western Digital”.
Model no.s might or might not be present. A camera’s model number written as one of the following variants: DSC-WX650 vs DSCWX650 vs DSC WX 650 vs WX 650.
Not much description. A product simply written as “Apple iPhone” without any mention of its generation, or other features.
Not many distinguishable features. Example, “Samsung Galaxy Note vs Samsung Galaxy Note 2”, “Apple ipad 3 16 GB wifi+cellular vs Apple ipad mini 16 GB wifi-cellular”
Factually Weak
Products represented with generic and subjective descriptions.
Colours and their combinations might be represented differently. Examples, “Puma Red Striped Bag”, “Adidas Black/Red/Blue Polo Tshirt”.
In the absence of clean, sufficient, and specific product information, the quality of algorithmic matching suffers. Product matching include many knobs and switches to adjust the weights given to different product attributes. For example, we might include a rule that says, “if two products are identical, then they fall in the same price range.” While such rules work well generally, they vary widely from category to category and across geographies. Further, adding more and more specific rules will start throwing off the algorithms in unexpected ways rendering them less effective.
In this post, we discussed the challenges posed by product matching that make it a hard problem to crack. In the next post, we will discuss how we address these challenges to make PriceWeave’s product matching robust.
PriceWeave is an all-around Competitive Intelligence product for retailers, brands, and manufacturers. We’re built on top of huge amounts of products data to provide real-time actionable insights. PriceWeave’s offerings include: pricing intelligence, assortment intelligence, gaps in catalogs, and promotion analysis. Please visit PriceWeave to view all our offerings. If you’d like to try us out request for a demo.
Research shows that the colour of the clothes we wear significantly affect our day to day lives. For instance wearing black might help us appear powerful and authoritative at the workplace, while a red dress can make us look more attractive to a date. A yellow top might brighten up one’s day and a blue one land us a nifty bonus.
Oftentimes buyers navigating the myriad nuances of current fashion look for help from friends, popular media and retailers themselves. Retailers, for their part, try to stay ahead of fashion trends by meticulously studying trends from magazines, keeping a close eye on competitors and wading through the chatter on social media and fashion blogs.
Now that most of retail is metrics driven and becoming smarter by the day, we asked ourselves whether there is a more optimal way to analyse the influence of colors on customer buying decisions. Here’s how we went about doing it:
Method:
Thanks to the internet, a huge mine of valuable fashion data is available to us through e-commerce sites, brand Pinterest pages and fashion blogs, which regularly update their content streams with the newest fashion offerings. Data ranging from featured fashion of the current season including the complete product catalogue of brands as well as combinations of dresses that go together (even between brands) are all available for us to collect and analyse.
By crawling these sites, pages and blogs periodically we can extract the colors on each of the images shared. This data is very helpful for any online/offline merchant to visualize the current trend in the market and plan out their own product offering. It is also possible to plot monthly data to capture the timeline of trends across different fashion websites.
How is it Useful?
Let us assess the applications made possible from this data. How would color analysis assist product managers, category heads and merchandising heads?
1.Spotting current trends:
Color analysis can spot current trends across brands and various filters. This gives decision makers the ability to gauge and respond to current trends and offerings. Some filters that can be used to analyse this are price, colors, categories, subcategories etc
2.Predictive trends:
Using historical color data future trends can be spotted with greater accuracy. With this data decision makers can stay ahead of the demands and the predictions of the market and gain a foothold on the ever changing nature of fashion.
3.Assortment Analysis:
Assortment Analysis can become more in depth and insightful with color analysis. Assortment comparisons of one’s offerings v/s competitor’s offerings can give a clear cut decision pointers on both one’s color offerings present and categories one can focus on to get ahead of the competition.
4.Recommendations
A strong recommendation feature is vital in driving up sales by offering the right products to buyers at the right time. Analysis of colors helps recommendations become smarter and more relevant. For instance, the algorithm can help understand what tops go with which jeans or which shirts go with what ties.
Colours add a new dimension to current business analytics. Decision makers will be able to access enhanced analytics on existing products and compare across sources based on parameters such as price, categories, subcategories etc.
Color Analysis in retail is largely unexplored and rife with possibilities. Doing it at scale presents a number of unique challenges that we are addressing. We’re excited to bring novel techniques and the power of large scale data analytics to retail.
Color analysis will add to a retailer’s understanding of consumer buying patterns. This will help retailers sell better and improve profit margins. We are currently working on integrating this feature into PriceWeave so that our customers can do a comparative assortment analysis with color as an additional dimension.
About Priceweave:
PriceWeave provides Competitive Intelligence for retailers, brands, and manufacturers. We’re built on top of huge amounts of products data to provide features such as: pricing opportunities (and changes), assortment intelligence, gaps in catalogs, reporting and analytics, and tracking promotions, and product launches. PriceWeave lets you track any number of products across any number of categories against your competitors. If you’d like to try us out request for a demo.
Picture this. You’re approaching the biggest sale of the year for your business, the number of offerings are ever growing and your competitors are inching in on your turf. How then are you to tackle the complex & challenging task of pricing your offerings? In short how do you know if the price is right?
Here’s how we think it’s possible:
1. Prioritize your objectives
Pricing can be modified based on your priorities. A good pricing intelligence tool lets you understand pricing opportunities across different dimensions (categories/brands, etc.). Which categories do you want to score on? Which price battles do you choose to fight? Once you have decided your focus areas, you can make pricing decisions accordingly.
2. Trading off margins for market share (or vice versa)
Trading off profits for larger market shares often decreases overhead and increases profits due to network effects. This means that the value of your offerings increases as more people use them (e.g., the iOS or the Windows platform). If margins are crucial do not hesitate to make smart and aggressive pricing decisions using inputs from pricing intelligence tools.
3. Avoiding underpricing and overpricing
Underpricing brings down the bottom line and overpricing alienates customers. Walking the thin line between these is both an art and a science. An effective path to a balanced pricing is employing a pricing intelligence tool. A pricing intelligence tool helps you in getting the price right with ease for any number of your products.
4. Understanding consumers and balancing costs
Who IS your buyer? How much is she willing to shell out for the products you are selling? How much should you mark up your products to recuperate your costs? What can you do retain your consumers and attract new ones? What steps are my competitors taking to achieve this (discounts/combos/coupons/loyalty points)? Answer these questions and you are closer to the ideal price.
5. Monitor competition
The simplest and the most effective way to price your product right is to monitor your competitors. Every pricing win contributes to your profits and boosts your bottom line. Competitive Intelligence products let you monitor your products across any of your competitors.
Conclusion
There are many tips on how to price your products. An effective pricing tool goes a long way in helping you determine the right price for your products. It augments your experience, intuition, and your internal analytics with solid competitive pricing data.
Why not give pricing intelligence a test ride then? Email us today at contact@dataweave.in to get started.
About PriceWeave
PriceWeave provides Competitive Intelligence for retailers, brands, and manufacturers. We’re built on top of huge amounts of products data to provide features such as: pricing opportunities (and changes), assortment intelligence, gaps in catalogs, reporting and analytics, and tracking promotions, and product launches. PriceWeave lets you track any number of products across any number of categories against your competitors. If you’d like to try us out request for a demo.
In retail, product assortment plays a critical role in selling effectively. It impacts the everyday decision making of category managers, brand managers, the merchandising, planning, and logistics teams. A good assortment mix helps achieve the following objectives:
Reduce acquisition costs for new customers (as well as retain existing customers)
Increase penetration by catering to a variety of customer segments
Optimize planning and inventory management costs.
Increasingly, retailers are moving away from a generic one-size-fits all assortment planning model, to a more dynamic and data driven approach. As a result, assortment benchmarking followed by assortment planning are activities that take place round the year. The breadth and depth of one’s assortment achieved through assortment benchmarking can define how and when products get bought.
A number of factors are crucial for assortment planning: analytics over internal data, intuition, experience, and understanding gained through trends. In addition to these, tracking assortment changes on competitors’ websites helps retailers track and adjust their product mix by adjusting features such as brands, colors, variants, and pricing. The goal is to help users find exactly what they are looking for, the moment they are looking for it.
Let’s see how we can achieve this through Assortment Intelligence tools in a moment. But first, some basics.
What is Assortment Intelligence?
Assortment intelligence refers to online retailers tracking, analysing a competitor’s assortment, and benchmarking it against one’s one assortment. Assortment intelligence tools make this process efficient. A good assortment intelligence tool such as PriceWeave gives you information the breadth and depth of your competitors’ assortment across categories and brands. It helps you analyze assortment through different lenses: colors, variants, sizes, shapes, and other technical specifications. With the help of an assortment intelligence tool, a retailer can get a good understanding about what products competitors have, how they perform and whether they should add these products to their existing catalog.
Who uses Assortment Intelligence?
Assortment tracking is used by retailers operating across categories as varied as footwear, electronics, jewelry, household goods,appliances, accessories, tools, handbags, furniture, clothing, baby products, and books among others.
Some Uses of Assortment Intelligence
Gaps in Catalog: Discover products/brands your competitors are offering that are not on your catalog, and add them.
Unique Offerings: Find products/brands that only you are offering and decide whether you are pricing them right. May be you want to bump up their prices.
Compare and analyze product assortment across dimensions: Benchmark your assortments across different dimensions and combinations thereof. Understand your as well as competitors’ focus areas. You can do this in aggregate as well as at the category/brand/feature level. Below we show a few examples.
Effectively measure discount distributions across brands and/or sources. Understand your competitors’ “sweet spots” in terms of discounts.
Understand assortment spread across price ranges. Are you focusing on all price ranges or only a few? Is that a decision you made consciously?
Deep dive using smart filters — monitor specific competitors, brands and sets of products with filters such as colors, variants, sizes and other product features.
Why do it?
Assortment Intelligence not only increases sales and improves margins, but also helps reduce planning and inventory costs. It allows retailers to strike the right balance between assortment and inventory while maximizing sales. Retailers can take informed decisions by analyzing one’s own as well as competitors’ assortments. Businesses gain an edge by identifying opportunities around changes in product mix and make quick decisions. By identifying areas that need focus, and taking timely actions, an assortment intelligence tool will help improve the bottom line.
What does PriceWeave bring in?
With a feature-rich product such as PriceWeave, you can do all of the above and more everyday (or more frequently if you like). In addition, you can get all assortment related data as reports in case you want to do your own analysis. You can also set alerts on any changes that you want to track.
PriceWeave lets you drill down as deep as you like. Assortments do not have to be based on high level dimensions or standard features like colors and sizes. You can analyze assortments based on technical specs of products (RAM size, cloth material, style, shape, etc.) or their combinations.
Assortment Intelligence is an important part of the PriceWeave offering. If you’d like us to help you make smarter assortment intelligence decisions talk to us
About Priceweave
PriceWeave provides Competitive Intelligence for retailers, brands, and manufacturers. We’re built on top of huge amounts of products data to provide features such as: pricing opportunities (and changes), assortment intelligence, gaps in catalogs, reporting and analytics, and tracking promotions, and product launches. PriceWeave lets you track any number of products across any number of categories against your competitors. If you’d like to try us out request for a demo.
In today’s world dominated by technology and gadgets, we often wonder how well a particular product or technology is being perceived by society and if it is truly going to leave a long lasting impression. We regularly see fan wars breaking out between Apple and Android fanatics (no offence to Windows mobile lovers!) on social media where each claim that they are better than the rest. Another thing we notice quite often is that whenever a new product is launched or in the process of being launched, it starts trending on different social media websites.
This made me wonder if there was a way to see some of these trends and the impact it is causing on social media. There are different social media channels where people post a wide variety of content ranging from personal opinions to videos and pictures. A few of the popular ones are listed below.
Facebook
Twitter
YouTube
Instagram
Pinterest
Today, I will discuss two such ways we can do this, namely Google Trendsand YouTube. If you want to know how we can perform data mining using Twitter, refer to my earlier post “Building a Twitter Sentiment Analysis App using R” which deals with getting data from Twitter and analyzing it.
Now, we will be looking at how easy it is to visualize trending topics on Google Trends without writing a single line of code. For this, you need to go to the Google Trends website. On opening it, you will be greeted by an interactive dashboard, showing the current trending topics summarized briefly just like the snapshot shown below. You can also click on any particular panel to explore it in detail.
This is not all that Google Trends has to offer. We can also customize visualizations to see how specific topics are trending across the internet by specifying then in the interface and the results are shown in the form of a beautiful visualization. Google gets the data based on the number of times people have searched for it online. A typical comparison of people’s interest in different mobile operating systems over time is shown below.
Interestingly, from the above visualization, we see that ‘Windows Mobile’ was quite popular from 2007 till mid 2009 when the popularity of ‘Android’ just skyrocketed. Apple’s ‘iOS’ gained popularity sometime around 2010. One must remember however that this data is purely based on data tracked by Google searches.
Coming to YouTube, it is perhaps the most popular video sharing website and I am sure all of you have at least watched a video on YouTube. Interestingly, we can also get a lot of interesting statistics from these videos besides just watching them, thanks to some great APIs provided by Google.
In the next part, I will discuss how to get interesting statistics from YouTube based on a search keyword and do some basic analysis. I won’t be delving into the depths of data analytics here but I will provide you enough information to get started with data mining from YouTube. We will be using Google’s YouTube Data API, some Python wrappers for the same and the pandasframework to analyze the data.
First, we would need to go to the Google Developers Console and create a new project just like the snapshot shown below.
Once the project is created, you will be automatically re-directed to the dashboard for the project: There you can choose to enable the APIs you want for your application. Go to the APIs section on the left and enable the YouTube Data API v3 just like it is depicted in the snapshot below (click it if you are unable to make out the text in the image).
Now, we will create a new API key for public API access. For this, go to the Credentials section and click on Create new key and choose the Server key option and create a new API key which is shown in the snapshot below (click to zoom the image).
We will be using some Python libraries so open up your terminal or command prompt and install the following necessary libraries if you don’t have them.
Now that the initial setup is complete, we can start writing some code! Head over to your favorite Python IDE or console and use the following code segment to build a YouTube resource object.
from apiclient.discovery import build from apiclient.errors import HttpError import pandas as pd DEVELOPER_KEY = "REPLACE WITH YOUR KEY" YOUTUBE_API_SERVICE_NAME = "youtube" YOUTUBE_API_VERSION = "v3" youtube = build(YOUTUBE_API_SERVICE_NAME, YOUTUBE_API_VERSION, developerKey=DEVELOPER_KEY)
Once this is complete, we will be using this YouTube resource object to search for videos with the android keyword. For this we will be using the search method to query YouTube and after getting back a list of results, we will be storing each result to its appropriate list, and then display the lists of matching videos, channels, and playlists using the following code segment.
Based on the query I ran, most of the results seemed to be videos. The output I obtained is shown in the snapshot below.
Since the playlists and channels we obtained are very less in number, I decided to go ahead and analyze the videos obtained. For that, we create a dict of video identifiers and video names. Then we pass a query to the YouTube API’s videos method, to get the relevant statistics for each video.
videos = {} for search_result in search_response.get("items", []): if search_result["id"]["kind"] == "youtube#video": videos[search_result["id"]["videoId"]] = search_result["snippet"]["title"] video_ids_list = ','.join(videos.keys()) video_list_stats = youtube.videos().list( id=video_ids_list, part='id,statistics' ).execute()
I know you must be interested by now to see what kind of data is present in video_list_stats. So for that, I will show you the relevant statistics obtained for a video from the API in the following snapshot.
Now we will be using pandas to analyze this data. For that, the following code segment is used, to get this data into a pandas data frame.
df = [] for item in videos_list_stats['items']: video_dict = dict(video_id = item['id'], video_title = videos[item['id']]) video_dict.update(item['statistics']) df.append(video_dict) df = pd.DataFrame.from_dict(df)
Now, we can view the contents of this data frame. I will be showing the output of the first few rows with the relevant columns in the snapshot below. I have considered only the important data points which include viewCount, likeCount, dislikeCount, commentCount indicating the number of views, likes, dislikes and comments on the videos respectively.
Once we have this table of clean and formatted data, we can do all sorts of analytics on it, like getting the mean and median for number of views, seeing which are really popular videos and so on. Some examples with required code segments are depicted below. I have used the IPython shell for analyzing the data.
Mean and Median of different counts
Top ten most viewed videos
Top ten most liked videos
Line chart showing counts of likes, views and comments
Thus you can see by now that a lot of interesting analysis and visualizations can be built on top of this data. For more details on how to customize and use the Youtube API with Python, you can refer to this page for sample code segments.
Twitter, as we know, is a highly popular social networking and micro-blogging service used by millions worldwide. Each status or tweet as we call it is a 140 character text message. Registered users can read and post tweets, but unregistered users can only view them. Text mining and sentiment analysis are some of the hottest topics in the analytics domain these days. Analysts are always looking to crunch thousands of tweets to gain insights on different topics, be it popular sporting events such as the FIFA World Cup or to know when the next product is going to be launched by Apple.
Today, we are going to see how we can build a web app for doing sentiment analysis of tweets using R, the most popular statistical language. For building the front end, we are going to be using the ‘Shiny’ package to make our life easier and we will be running R code in the backend for getting tweets from twitter and analyzing their sentiment.
The first step would be to establish an authorized connection with Twitter for getting tweets based on different search parameters. For doing that, you can follow the steps mentioned in this document which includes the R code necessary to achieve that.
After obtaining a connection, the next step would be to use the ‘shiny’ package to develop our app. This is a web framework for R, developed by RStudio. Each app contains a server file ( server.R ) for the backend computation and a user interface file ( ui.R ) for the frontend user interface. You can get the code for the app from my github repository here which is fairly well documented but I will explain the main features anyway.
The first step would be to develop the UI of the application, you can take a look at the ui.R file, we have a left sidebar, where we take input from the user in two text fields for either twitter hashtags or handles for comparing the sentiment. We also create a slider for selecting the number of tweets we want to retrieve from twitter. The right panel consists of four tabs, here we display the sentiment plots, word clouds and raw tweets for both the entities in respective tabs as shown below.
Coming to the backend, remember to also copy the two dictionary files, ‘negative_words.txt’ and ‘positive_words.txt’ from the repository because we will be using them for analyzing and scoring terms from tweets. On taking a close look at the server.R file, you can notice the following operations taking place.
– The ‘TweetFrame’ function sends the request query to Twitter, retrieves the tweets and aggregates it into a data frame. — The ‘CleanTweets’ function runs a series of regexes to clean tweets and extract proper words from them. — The ‘numoftweets’ function calculates the number of tweets. — The ‘wordcloudentity’ function creates the word clouds from the tweets. — The ‘sentimentalanalysis’ and ‘score.sentiment’ functions performs the sentiment analysis for the tweets.
These functions are called in reactive code segments to enable the app to react instantly to change in user input. The functions are documented extensively but I’ll explain the underlying concept for sentiment analysis and word clouds which are generated.
For word clouds, we get the text from all the tweets, remove punctuation and stop words and then form a term document frequency matrix and sort it in decreasing order to get the terms which occur the most frequently in all the tweets and then form a word cloud figure based on those tweets. An example obtained from the app is shown below for hashtags ‘#thrilled’ and ‘#frustrated’.
For sentiment analysis, we use Jeffrey Breen’s sentiment analysis algorithm cited here, where we clean the tweets, split tweets into terms and compare them with our positive and negative dictionaries and determine the overall score of the tweet from the different terms. A positive score denoted positive sentiment, a score of 0 denotes neutral sentiment and a negative score denotes negative sentiment. A more extensive and advanced n-gram analysis can also be done but that story is for another day. An example obtained from the app is shown below for hashtags ‘#thrilled’ and ‘#frustrated’.
After getting the server and UI code, the next step is to deploy it in the server, we will be using shinyapps.io server which allows you to host your R web apps free of charge. If you already have the code loaded up in RStudio, you can deploy it from there using the ‘deployApp()’ command.
GNU Parallel is a tool that can be deployed from a shell to parallelize job execution. A job can be anything from simple shell scripts to complex interdependent Python/Ruby/Perl scripts. The simplicity of ‘Parallel’ tool lies in it usage. A modern day computer with multicore processors should be enough to run your jobs in parallel. A single core computer can also run the tool, but the user won’t be able to see any difference as the jobs will be context switched by the underlying OS.
At DataWeave, we use Parallel for automating and parallelizing a number of resource extensive processes ranging from crawling to data extraction. All our servers have 8 cores with capability of executing 4 threads in each. So, we experienced huge performance gain after deploying Parallel. Our in-house image processing algorithms used to take more than a day to process 200,000 high resolution images. After using Parallel, we have brought the time down to a little over 40 minutes!
GNU Parallel can be installed on any Linux box and does not require sudo access. The following command will install the tool:
(wget -O - pi.dk/3 || curl pi.dk/3/) | bash
GNU Parallel can read inputs from a number of sources — a file or command line or stdin. The following simple example takes the input from the command line and executes in parallel:
parallel echo ::: A B C
The following takes the input from a file:
parallel -a somefile.txt echo
Or STDIN:
cat somefile.txt | parallel echo
The inputs can be from multiple files too:
parallel -a somefile.txt -a anotherfile.txt echo
The number of simultaneous jobs can be controlled using the — jobs or -j switch. The following command will run 5 jobs at once:
parallel --jobs 5 echo ::: A B C D E F G H I J
By default, the number of jobs will be equal to the number of CPU cores. However, this can be overridden using percentages. The following will run 2 jobs per CPU core:
parallel --jobs 200% echo ::: A B C D
If you do not want to set any limit, then the following will use all the available CPU cores in the machine. However, this is NOT recommended in production environment as other jobs running on the machine will be vastly slowed down.
parallel --jobs 0 echo ::: A B C
Enough with the toy examples. The following will show you how to bulk insert JSON documents in parallel in a MongoDB cluster. Almost always we need to insert millions of document quickly in our MongoDB cluster and inserting documents serially doesn’t cut it. Moreover, MongoDB can handle parallel inserts.
The following is a snippet of a file with JSON document. Let’s assume that there are a million similar records in the file with one JSON document per line.
That’s it! There are many switches and options available for advanced processing. They can be accessed by doing a man parallel on the shell. Also the following page has a set of tutorials: GNU Parallel Tutorials.
Let’s revisit our raison d’être: DataWeave is a platform on which we do large-scale data aggregation and serve this data in forms that are easily consumable. The nature of the data that we deal with is that: (1) it is publicly available on the web, (2) it is factual (to the extent possible), and (3) it has high utility (decided by a number of factors that we discuss below).
The primary access channel for our data are the Data API. Other access channels such as visualizations, reports, dashboards, and alerting systems are built on top of our data APIs. Data Products such as PriceWeave, are built by combining multiple APIs and packaging them with reporting and analytics modules.
Even as the platform is capable of aggregating any kind of data on the web, we need to prioritize the data that we aggregate, and the data products that we build. There are a lot of factors that help us in deciding what kinds of data we must aggregate and the APIs we must provide on DataWeave. Some of these factors are:
Business Case: A strong business use-case for the API. There has to be an inherent pain point the data set must solve. Be it the Telecom Tariffs AP or Price Intelligence API — there are a bunch of pain points they solve for distinct customer segments.
Scale of Impact: There has to exist a large enough volume of potential consumers that are going through the pain points, that this data API would solve. Consider the volume of the target consumers for the Commodity Prices API, for instance.
Sustained Data Need: Data that a consumer needs frequently and/or on a long term basis has greater utility than data that is needed infrequently. We look at weather and prices all the time. Census figures, not so much.
Assured Data Quality: Our consumers need to be able to trust the data we serve: data has to be complete as well as correct. Therefore, we need to ensure that there exist reliable public sources on the Web that contain the data points required to create the API.
Once these factors are accounted for, the process of creating the APIs begins. One question that we are often asked is the following: how easy/difficult is it to create data APIs? That again depends on many factors. There are many dimensions to the data we are dealing with that helps us in deciding the level of difficulty. Below we briefly touch upon some of those:
1. Structure: Textual data on the Web can be structured/semi-structured/unstructured. Extracting relevant data points from semi-structured and unstructured data without the existence of a data model can be extremely tricky. The process of recognizing the underlying pattern, automating the data extraction process, and monitoring accuracy of extracted data becomes difficult when dealing with unstructured data at scale.
2. Temporality: Data can be static or temporal in nature. Aggregating static datas sets is an one time effort. Scenarios where data changes frequently or new data points are being generated pose challenges related to scalability and data consistency. For e.g., The India Local Mandi Prices AP gets updated on a day-to-day basis with new data being added. When aggregating data that is temporal, monitoring changes to data sources and data accuracy becomes a challenge. One needs to have systems in place that ensure data is aggregated frequently and also monitored for accuracy.
3. Completeness: At one end of the spectrum we have existing data sets that are publicly downloadable. On the other end, we have data points spread across sources. In order to create data sets over these data points, these data points need to be aggregated and curated in order for them to be used. These data sources publish data in their own format, update them at different intervals. As always, “the whole is larger than the sum of its parts”; these individual data points when aggregated and presented together have many more use cases than those for the individual data points themselves.
4. Representations: Data on the Web exists in various formats including (if we are particularly unlucky!) non-standard/proprietary ones. Data exists in HTML, XML, XLS, PDFs, docs, and many more. Extracting data from these different formats and presenting them through standard representations comes with its own challenges.
5. Complexity: The data sets wherein data points are independent of each other are fairly simple to reason about. On the other hand, consider network data sets such as social data, maps, and transportation networks. The complexity arises due to the relationships that can exist between data points within and across data sets. The extent of pre-processing required to analyse these relationships makes these data sets is huge even on a small scale.
6 .(Pre/Post) Processing: There is a lot of pre-processing involved to make raw crawled data presentable and accessible through a data API. This involves, cleaning, normalization, and representing data in standard forms. Once we have the data API, there can be a number of way that this data can be processed to create new and interesting APIs.
So, that at a high level, is the way we work at DataWeave. Our vision is that of curating and providing access to all of the world’s public data. We are progressing towards this vision one API at a time.
Several months ago we released our Telecom recharge plans API. It soon turned out to be one of our more popular APIs, with some of the leading online recharge portals using it extensively. (So, the next time you recharge your phone, remember us :))
In this post, we’ll talk in detail about the genesis of this API and the problem it is solving.
Before that — -and since we are into the business of building data products — some data points.
As you can see, most mobile phones in India are prepaid. That is to say, there is a huge prepaid mobile recharge market. Just how big is this market?
The above infographic is based on a recent report by Avendus [pdf]. Let’s focus on the online prepaid recharge market. Some facts:
There are around 11 companies that provide an online prepaid recharge service. Here’s the list: mobikwik, rechargeitnow, paytm, freecharge, justrechargeit, easymobilerecharge, indiamobilerecharge, rechargeguru, onestoprecharge, ezrecharge, anytimerecharge
RechargeItNow seems to be the biggest player. As of August 2013, they claimed an annual transactions worth INR 6 billion, with over 100000 recharges per day pan India.
PayTM, Freecharge, and Mobikwik seem to be the other big players. Freecharge claimed recharge volumes of 40000/day in June 2012 (~ INR 2 billion worth of transactions), and they have been growing steadily.
Telcos offer a commission of approximately 3% to third party recharge portals. So, it means there is an opportunity worth about 4 bn as of today.
Despite the Internet penetration in India being around 11%, only about 1% of mobile prepaid recharges happen online. This goes to show the huge opportunity that lies untapped!
It also goes to show why there are so many players entering this space. It’s only going to get crowded more.
What does all this have to do with DataWeave? Let’s talk about the scale of the “data problem” that we are dealing with here. Some numbers that give an estimate on this.
There are 13 cellular service providers in India. Here’s the list: Aircel Cellular Ltd, Aircel Limited, Bharti Airtel, BSNL, Dishnet Wireless, IDEA (operates as Idea ABTL & Spice in different states), Loop Mobile, MTNL, Reliable Internet, Reliance Telecom, Uninor, Videocon, and Vodafone. There are 22 circles in India. (Not every service provider has operations in every circle.)
Find below the number of telecom recharge plans we have in our database for various operators.
In fact, you can see that between the last week and today, we have added about 300 new plans (including plans for a new operator).
The number of plans varies across operators. Vodafone, for instance, gives its users a huge number of options.
The plans vary based on factors such as: denomination, recharge value, recharge talktime, recharge validity, plan type (voice/data), and of course, circle as well as the operator.
For a third party recharge service provider, the below are a daily pain point:
plans become invalid on a regular basis
new plans are added on a regular basis
the features associated with a plan change (e.g, a ‘xx mins free talk time’ plan becomes ‘unlimited validity’ or something else)
We see that 10s of plans become invalid (and new ones introduced) every day. All third party recharge portals lose significant amount of money on a daily basis because: they might not have information about all the plans and they might be displaying invalid plans.
DataWeave’s Telecom Recharge Plans API solves this problem. This is how you use the API.
We aggregate plans from the various cellular service providers across all circles in India on a daily basis. One of our customers once mentioned that earlier they used to aggregate this data manually, and it used to take them about a month to do this. With our API, we have reduced the refresh cycle to one day.
In addition, now that this is process is automated, they can be confident that the data they present to their customers is almost always complete as well as accurate.
Want to try it out for your business? Talk to us! If you are a developer who wants to use this or any other APIs, we let you use them for free. Just sign upand get your API key.
DataWeave helps businesses make data-driven decisions by providing relevant actionable data. The company aggregates and organizes data from the web, such that businesses can access millions of data points through APIs, dashboards, and visualizations.
[The idea for this post was given by Mahesh B. L., who is a friend of DataWeave. We are going to have some fun with data below.]
Ayudha Puje (ಆಯುಧ ಪೂಜೆ) or Ayudha Puja marks the end of Navaratri. It is the day before Dasara. Ayudha Puje is essentially the worship of implements (or tools/weapons) that we use. While this is practiced in all southern states, since Dasara is Karnataka’s nADa habba (ನಾಡ ಹಬ್ಬ) or “the festival of the land” it is done so with added fervour in Karnataka. At least it does seem so if you see the roads for the next few days!
As part of Ayudha Puje, everybody washes and decorates their vehicles (well, they are our major weapons in more than one sense) and worships them by squashing a suitable amount of nice and juicy lemons with great vengeance. Ash gourds (Winter Melons) are also shattered with furious anger.
Just how many lemons got squashed on the last Ayudha Puje in and around Bangalore? We dug up some data from a few sources (Praja, Bangalore City Traffic Police, RTO, a Hindu article), and came with a quick and dirty estimate on the number of vehicles in Bangalore. We estimate that there are about 55 lac vehicles in and around Bangalore. You can download the data we used and our approximations here.
So, assuming one lemon per wheel, upwards of 13 million lemons got squashed on October 12, 2013 that could otherwise have been put to some good use such as preventing cauliflowers from turning brown, or serving Gin Fizz to an entire country. (Of course, we know that not everyone performs Ayudha Puje. But let’s not be insensitive to the plight of our victims by digressing.)
We don’t have an estimate on how many Ash Gourds were broken, but we are sure the quantity would have been at least enough to serve tasty Halwa to every person in Karnataka.
Do you want to do some serious analysis on Lemons and Pumpkins yourself? Take a look at our Commodity Prices API. Register to get an API key and start using it. Like this:
DataWeave helps businesses make data-driven decisions by providing relevant actionable data. The company aggregates and organizes data from the web, such that businesses can access millions of data points through APIs, dashboards, and visualizations.
In today’s world, the analysis of any social media stream can reap invaluable information about, well, pretty much everything. If you are a business catering to a large number of consumers, it is a very important tool for understanding and analyzing the market’s perception about you, and how your audience reacts to whatever you present before them.
At DataWeave, we sat down to create a setup that would do this for some e-commerce stores and retail brands. And the first social network we decided to track was the micro-blogging giant, Twitter. Twitter is a great medium for engaging with your audience. It’s also a very efficient marketing channel to reach out to a large number of people.
Data Collection
The very first issue that needs to be tackled is collecting the data itself. Now quite understandably, Twitter protects its data vigorously. However, it does have a pretty solid REST API for data distribution purposes too. The API is simple, nothing too complex, and returns data in the easy to use JSON format. Take a look at the timeline API, for example. That’s quite straightforward and has a lot of detailed information.
The issue with the Twitter API however, is that it is seriously rate limited. Every function can be called in a range of 15–180 times in a 15-minute window. While this is good enough for small projects not needing much data, for any real-world application however, these rate limits can be really frustrating. To avoid this, we used the Streaming API, which creates a long-lived HTTP GET request that continuously streams tweets from the public timeline.
Also, Twitter seems to suddenly return null values in the middle of the stream, which can make the streamer crash if we don’t take care. As for us, we simply threw away all null data before it reached the analysis phase, and as an added precaution, designed a simple e-mail alert for when the streamer crashed.
Data Storage
Next is data storage. Data is traditionally stored in tables, using RDBMS. But for this, we decided to use MongoDB, as a document store seemed quite suitable for our needs. While I didn’t have much clue about MongoDB or what purpose it’s going to serve at first, I realized that is a seriously good alternative to MySQL, PostgreSQL and other relational schema-based data stores for a lot of applications.
Some of its advantages that I very soon found out were: documents-based data model that are very easy to handle analogous to Python dictionaries, and support for expressive queries. I recommend using this for some of your DB projects. You can play about with it here.
Data Processing
Next comes data processing. While data processing in MongoDB is simple, it can also be a hard thing to learn, especially for someone like me, who had no experience anywhere outside SQL. But MongoDB queries are simple to learn once the basics are clear.
For example, in a DB DWSocial with a collection tweets, the syntax for getting all tweets would be something like this in a Python environment:
rt = list(db.tweets.find())
The list type-cast here is necessary, because without it, the output is simply a MongoDB reference, with no value. Now, to find all tweets where user_id is 1234, we have
rt = list(db.retweets.find({ 'user_id': 1234 })
Apart from this, we used regexes to detect specific types of tweets, if they were, for example, “offers”, “discounts”, and “deals”. For this, we used the Python re library, that deals with regexes. Suffice is to say, my reaction to regexes for the first two days was much like
Once again, its just initial stumbles. After some (okay, quite some) help from Thothadri, Murthy and Jyotiska, I finally managed a basic parser that could detect which tweets were offers, discounts and deals. A small code snippet is here for this purpose.
This is much less complicated than it seems. And it also brings us to our final step–integrating all our queries into a REST-ful API.
Data Serving
For this, mulitple web-frameworks are available. The ones we did consider were Flask, Django and Bottle.
Weighing the pros and cons of every framework can be tedious. I did find this awesome presentation on slideshare though, that succinctly summarizes each framework. You can go through it here.
We finally settled on Bottle as our choice of framework. The reasons are simple. Bottle is monolithic, i.e., it uses the one-file approach. For small applications, this makes for code that is easier to read and maintainable.
Some sample web address routes are shown here:
#show all tracked accounts
id_legend = {57947109 : 'Flipkart', 183093247: 'HomeShop18', 89443197: 'Myntra', 431336956: 'Jabong'}
@route('/ids')
def get_ids():
result = json.dumps(id_legend)
return result
#show all user mentions for a particular account @route(‘/user_mentions’)
def user_mention():
m = request.query.id
ac_id = int(m)
t = list(tweets.find({'created_at': { '$gte': fourteen_days_ago }, 'retweeted': 'no', 'user_id': { '$ne': ac_id} }))
a = len(t)
mylist = []
for i in t:
mylist.append({i.get('user_id'): i.get('id')})
x = { 'num_of_mentions': a, 'mentions_details': mylist }
result = json.dumps(x)
return result
This is how the DataWeave Social API came into being. I had a great time doing this, with special credits to Sanket, Mandar and Murthy for all the help that they gave me for this. That’s all for now, folks!
We have taken a special interest in colors in recent times. Some of us can even identify and name a couple of dozen different colors! The genesis for this project was PriceWeave’s Color Analytics offering. With Color Analytics, we provide detailed analysis in colors and other attributes related to retailers and brands in Apparel and Lifestyle products space.
The Idea
The initial idea was to simply extract the dominating colors from an image and generate a color palette. Fashion blogs and Pinterest pages are updated regularly by popular fashion brands and often feature their latest offerings for the current season and their newly released products. So, we thought if we can crawl these blogs periodically after every few days/weeks, we can plot the trends in graphs using the extracted colors. This timeline is very helpful for any online/offline merchant to visualize the current trend in the market and plan out their own product offerings.
We expanded this to include Apparel and Lifestyle products from eCommerce websites like Jabong, Myntra, Flipkart, and Yebhi, and stores of popular brands like Nike, Puma, and Reebok. We also used their Pinterest pages.
Color Extraction
The core of this work was to build a robust color extraction algorithm. We developed a couple of algorithms by extending some well known techniques. One approach we followed was to use standard unsupervised machine learning techniques. We ran k-means clustering against our images data. Here k refers to the number of colors we are trying to extract from the image.
In another algorithm, we extracted all the possible color points from the image and used heuristics to come up with a final set of colors as a palette.
Another of our algorithms was built on top of the Python Image Library (PIL) and the Colorific package to extract and produce the color palette from the image.
Regardless of the approach we used, we soon found out that both speed and accuracy were a problem. Our k-means implementation produced decent results but it took 3–4 seconds to process an entire image! This might not seem much for a small set of images, but the script took 2 days to process 40,000 products from Myntra.
Post this, we did a lot of tweaking in our algorithms and came up with a faster and more accurate model which we are using currently.
ColorWeave API
We have open sourced an early version of our implementation. It is available of github here. You can also download the Python package from the Python Package Index here. Find below examples to understand its usage.
Retrieve dominant colors from an image URL
from colorweave import palette print palette(url="image_url")
Retrive n dominant colors from a local image and print as json:
print palette(url="image_url", n=6, output="json")
Print a dictionary with each dominant color mapped to its CSS3 color name
print palette(url="image_url", n=6, format="css3")
Print the list of dominant colors using k-means clustering algorithm
print palette(url="image_url", n=6, mode="kmeans")
Data Storage
The next challenge was to come up with an ideal data model to store the data which will also let us query on it. Initially, all the processed data was indexed by Solr and we used its REST API for all our querying. Soon we realized that we have to come up with better data model to store, index and query the data.
We looked at a few NoSQL databases, especially column oriented stores like Cassandra and HBase and document stores like MongoDB. Since the details of a single product can be represented as a JSON object, and key-value storage can prove to be quite useful in querying, we settled on MongoDB. We imported our entire data (~ 160,000 product details) to MongoDB, where each product represents a single document.
Color Mapping
We still had one major problem we needed to resolve. Our color extraction algorithm produces the color palette in hexadecimal format. But in order to build a useful query interface, we had to translate the hexcodes to human readable color names. We had two options. Either we could use a CSS 2.0 web color names consisting on 16 basic colors (White, Silver, Gray, Black, Red, Maroon, Yellow, Olive, Lime, Green, Aqua, Teal, Blue, Navy, Fuchsia, Purple) or we could use CSS 3.0 web color names consisting of 140 colors. We used both to map colors and stored those colors along with each image.
Color Hierarchy
We mapped the hexcodes to CSS 3.1 which has every possible shades for the basic colors. Then we assigned a parent basic color for every shades and stored them separately. Also, we created two fields — one for the primary colors and the other one for the extended colors which will help us in indexing and querying. At the end, each product had 24 properties associated with it! MongoDB made it easier to query on the data using the aggregation framework.
What next?
A few things. An advanced version of color extraction (with a number of other exciting features) is being integrated into PriceWeave. We are also working on building a small consumer facing product where users will be able to query and find products based on color and other attributes. There are many other possibilities some of which we will discuss when the time is ripe. Signing off for now!
Without argument, one of the most common used data model is JSON. There are two popular packages used for handling json — first is the stockjsonpackage that comes with default installation of Python, the other one issimplejson which is an optimized and maintained package for Python. The goal of this blog post is to introduce ultrajson or Ultra JSON, a JSON library written mostly in C and built to be extremely fast.
We have done the benchmark on three popular operations — load, loadsanddumps. We have a dictionary with 3 keys — id, name and address. We will dump this dictionary using json.dumps() and store it in a file. Then we will use json.loads() and json.load() separately to load the dictionaries from the file. We have performed this experiment on 10000, 50000, 100000,200000, 1000000 dictionaries and observed how much time it takes to perform the operation by each library.
DUMPS OPERATION LINE BY LINE
Here is the result we received using the json.dumps() operations. We have dumped the content dictionary by dictionary.
We notice that json performs better than simplejson but ultrajson wins the game with almost 4 times speedup than stock json.
DUMPS OPERATION (ALL DICTIONARIES AT ONCE)
In this experiment, we have stored all the dictionaries in a list and dumped the list using json.dumps().
simplejson is almost as good as stock json, but again ultrajson outperforms them by more than 60% speedup. Now lets see how they perform for load and loads operation.
LOAD OPERATION ON A LIST OF DICTIONARIES
Now we do the load operation on a list of dictionaries and compare the results.
Surprisingly, simplejson beats other two, with ultrajson being almost close to simplejson. Here, we observe that simplejson is almost 4 times faster than stock json, same with ultrajson.
LOADS OPERATION ON DICTIONARIES
In this experiment, we load dictionaries from the file one by one and pass them to the json.loads() function.
Again ultrajson steals the show, being almost 6 times faster than stock json and 4 times faster than simplejson.
That is all the benchmarks we have here. The verdict is pretty clear. Use simplejson instead of stock json in any case, since simplejson is well maintained repository. If you really want something extremely fast, then go for ultrajson. In that case, keep in mind that ultrajson only works with well defined collections and will not work for un-serializable collections. But if you are dealing with texts, this should not be a problem.
[This post was written by Dipanjan. Dipanjan works in the Engineering Team with Mandar, addressing some of the problems related to Data Semantics. He loves watching English Sitcoms in his spare time. This was originally posted on the PriceWeave blog.]
This is the second post in our series of blog posts which we shall be presenting regarding social media analysis. We have already talked about Twitter Mining in depth earlier and also how to analyze social trends in general and gather insights from YouTube. If you are more interested in developing a quick sentiment analysis app, you can check our short tutorial on that as well.
Our flagship product, PriceWeave, is all about delivering real time actionable insights at scale. PriceWeave helps Retailers and Brands take decisions on product pricing, promotions, and assortments on a day to day basis. One of the areas we focus on is “Social Intelligence”, where we measure our customers’ social presence in terms of their reach and engagement on different social channels. Social Intelligence also helps in discovering brands and products trending on social media.
Today, I will be talking about how we can get data from Twitter in real-time and perform some interesting analytics on top of that to understand social reactions to trending brands and products.
In our last post, we had used Twitter’s Search API for getting a selective set of tweets and performed some analytics on that. But today, we will be using Twitter’s Streaming API, to access data feeds in real time. A couple of differences with regards to the two APIs are as follows. The Search API is primarily a REST API which can be used to query for “historical data”. However, the Streaming API gives us access to Twitter’s global stream of tweets data. Moreover, it lets you acquire much larger volumes of data with keyword filters in real-time compared to normal search.
Installing Dependencies
I will be using Python for my analysis as usual, so you can install it if you don’t have it already. You can use another language of your choice, but remember to use the relevant libraries of that language. To get started, install the following packages, if you don’t have them already. We use simplejson for JSON data processing at DataWeave, but you are most welcome to use the stock json library.
Acquiring Data
We will use the Twitter Streaming API and the equivalent python wrapper to get the required tweets. Since we will be looking to get a large number of tweets in real time, there is the question of where should we store the data and what data model should be used. In general, when building a robust API or application over Twitter data, MongoDB being a schemaless document-oriented database, is a good choice. It also supports expressive queries with indexing, filtering and aggregations. However, since we are going to analyze a relatively small sample of data using pandas, we shall be storing them in flat files.
Note: Should you prefer to sink the data to MongoDB, the mongoexportcommand line tool can be used to export it to a newline delimited format that is exactly the same as what we will be writing to a file.
The following code snippet shows you how to create a connection to Twitter’s Streaming API and filter for tweets containing a specific keyword. For simplicity, each tweet is saved in a newline-delimited file as a JSON document. Since we will be dealing with products and brands, I have queried on two trending products and brands respectively. They are, ‘Sony’ and ‘Microsoft’ with regards to brands and ‘iPhone 6’ and ‘Galaxy S5’ with regards to products. You can write the code snippet as a function for ease of use and call it for specific queries to do a comparative study.
Let the data stream for a significant period of time so that you can capture a sizeable sample of tweets.
Analyses and Visualizations
Now that you have amassed a collection of tweets from the API in a newline delimited format, let’s start with the analyses. One of the easiest ways to load the data into pandas is to build a valid JSON array of the tweets. This can be accomplished using the following code segment.
Note: With pandas, you will need to have an amount of working memory proportional to the amount of data that you’re analyzing.
Once you run this, you should get a dictionary containing 4 data frames. The output I obtained is shown in the snapshot below.
Note: Per the Streaming API guidelines, Twitter will only provide up to 1% of the total volume of real time tweets, and anything beyond that is filtered out with each “limit notice”.
The next snippet shows how to remove the “limit notice” column if you encounter it.
Time-based Analysis
Each tweet we captured had a specific time when it was created. To analyze the time period when we captured these tweets, let’s create a time-based index on the created_at field of each tweet so that we can perform a time-based analysis to see at what times do people post most frequently about our query terms.
The output I obtained is shown in the snapshot below.
I had started capturing the Twitter stream at around 7 pm on the 6th of December and stopped it at around 11:45 am on the 7th of December. So the results seem consistent based on that. With a time-based index now in place, we can trivially do some useful things like calculate the boundaries, compute histograms and so on. Operations such as grouping by a time unit are also easy to accomplish and seem a logical next step. The following code snippet illustrates how to group by the “hour” of our data frame, which is exposed as a datetime.datetime timestamp since we now have a time-based index in place. We print an hourly distribution of tweets also just to see which brand \ product was most talked about on Twitter during that time period.
The outputs I obtained are depicted in the snapshot below.
The “Hour” field here follows a 24 hour format. What is interesting here is that, people have been talking more about Sony than Microsoft in Brands. In Products, iPhone 6 seems to be trending more than Samsung’s Galaxy S5. Also the trend shows some interesting insights that people tend to talk more on Twitter in the morning and late evenings.
Time-based Visualizations
It could be helpful to further subdivide the time ranges into smaller intervals so as to increase the resolution of the extremes. Therefore, let’s group into a custom interval by dividing the hour into 15-minute segments. The code is pretty much the same as before except that you call a custom function to perform the grouping. This time, we will be visualizing the distributions using matplotlib.
The two visualizations are depicted below. Of course don’t forget to ignore the section of the plots from after 11:30 am to around 7 pm because during this time no tweets were collected by me. This is indicated by a steep rise in the curve and is insignificant. The real regions of significance are from hour 7 to 11:30 and hour 19 to 22.
Considering brands, the visualization for Microsoft vs. Sony is depicted below. Sony is the clear winner here.
Considering products, the visualization for iPhone 6 vs. Galaxy S5 is depicted below. The clear winner here is definitely iPhone 6.
Tweeting Frequency Analysis
In addition to time-based analysis, we can do other types of analysis as well. The most popular analysis in this case would be frequency based analysis of the users authoring the tweets. The following code snippet will compute the Twitter accounts that authored the most tweets and compare it to the total number of unique accounts that appeared for each of our query terms.
The results which I obtained are depicted below.
What we do notice is that a lot of these tweets are also made by bots, advertisers and SEO technicians. Some examples are Galaxy_Sleeves and iphone6_sleeves which are obviously selling covers and cases for the devices.
Tweeting Frequency Visualizations
After frequency analysis, we can plot these frequency values to get better intuition about the underlying distribution, so let’s take a quick look at it using histograms. The following code snippet created these visualizations for both brands and products using subplots.
The visualizations I obtained are depicted below.
The distributions follow the “Pareto Principle” as expected where we see that a selective number of users make a large number of tweets and the majority of users create a small number of tweets. Besides that, we see that based on the tweet distributions, Sony and iPhone 6 are more trending than their counterparts.
Locale Analysis
Another important insight would be to see where your target audience is located and their frequency. The following code snippet achieves the same.
The outputs which I obtained are depicted in the following snapshot. Remember that Twitter follows the ISO 639–1 language code convention.
The trend we see is that most of the tweets are from English speaking countries as expected. Surprisingly, most of the Tweets regarding iPhone 6 are from Japan!
Analysis of Trending Topics
In this section, we will see some of the topics which are associated with the terms we used for querying Twitter. For this, we will be running our analysis on the tweets where the author speaks in English. We will be using the nltklibrary here to take care of a couple of things like removing stopwords which have little significance. Now I will be doing the analysis here for brands only, but you are most welcome to try it out with products too because, the following code snippet can be used to accomplish both the computations.
What the above code does is that, it takes each tweet, tokenizes it and then computes a term frequency and outputs the 20 most common terms for each brand. Of course an n-gram analysis can give a deeper insight into trending topics but the same can also be accomplished with ntlk’s collocations function which takes in the tokens and outputs the context in which they were mentioned. The outputs I obtained are depicted in the snapshot below.
Some interesting insights we see from the above outputs are as follows.
Sony was hacked recently and it was rumored that North Korea was responsible for that, however they have denied that. We can see that is trending on Twitter in context of Sony. You can read about it here.
Sony has recently introduced Project Sony Skylight which lets you customize your PS4.
There are rumors of Lumia 1030, Microsoft’s first flagship phone.
People are also talking a lot about Windows 10, the next OS which is going to be released by Microsoft pretty soon.
Interestingly, “ebay price” comes up for both the brands, this might be an indication that eBay is offering discounts for products from both these brands.
To get a detailed view on the tweets matching some of these trending terms, we can use nltk’s concordance function as follows.
The outputs I obtained are as follows. We can clearly see the tweets which contain the token we searched for. In case you are unable to view the text clearly, click on the image to zoom.
Thus, you can see that the Twitter Streaming API is a really good source to track social reaction to any particular entity whether it is a brand or a product. On top of that, if you are armed with an arsenal of Python’s powerful analysis tools and libraries, you can get the best insights from the unending stream of tweets.
That’s all for now folks! Before I sign off, I would like to thank Matthew A. Russell and his excellent book Mining the Social Web once again, without which this post would not have been possible. Cover image credit goes to TechCrunch.
Due to the massive growth of social media in the last decade, it has become a rage among data enthusiasts to tap into the vast pool of social data and gather interesting insights like trending items, reception of newly released products by society, popularity measures to name a few.
We are continually evolving PriceWeave, which has the most extensive set of offerings when it comes to providing actionable insights to retail stores and brands. As part of the product development, we look at social data from a variety of channels to mine things like: trending products/brands; social engagement of stores/brands; what content “works” and what does not on social media, and so forth.
We do a number of experiments with mining Twitter data, and this series of blog posts is one of the outputs from those efforts.
In some of our recent blog posts, we have seen how to look at current trends and gather insights from YouTube the popular video sharing website. We have also talked about how to create a quick bare-bones web application to perform sentiment analysis of tweets from Twitter. Today I will be talking about mining data from Twitter and doing much more with it than just sentiment analysis. We will be analyzing Twitter data in depth and then we will try to get some interesting insights from it.
To get data from twitter, first we need to create a new Twitter application to get OAuth credentials and access to their APIs. For doing this, head over to the Twitter Application Management page and sign in with your Twitter credentials. Once you are logged in, click on the Create New App button as you can see in the snapshot below. Once you create the application, you will be able to view it in your dashboard just like the application I created, named DataScienceApp1_DS shows up in my dashboard depicted below.
On clicking the application, it will take you to your application management dashboard. Here, you will find the necessary keys you need in the Keys and Access Tokens section. The main tokens you need are highlighted in the snapshot below.
I will be doing most of my analysis using the Python programming language. To be more specific, I will be using the IPython shell, but you are most welcome to use the language of your choice, provided you get the relevant API wrappers and necessary libraries.
Installing necessary packages
After obtaining the necessary tokens, we will be installing some necessary libraries and packages, namely twitter, prettytable and matplotlib. Fire up your terminal or command prompt and use the following commands to install the libraries if you don’t have them already.
Creating a Twitter API Connection
Once the packages are installed, you can start writing some code. For this, open up the IDE or text editor of your choice and use the following code segment to create an authenticated connection to Twitter’s API. The way the following code snippet works, is by using your OAuth credentials to create an object called auth that represents your OAuth authorization. This is then passed to a class called Twitter belonging to the twitter library and we create a resource object named twitter_api that is capable of issuing queries to Twitter’s API.
If you do a print twitter_api and all your tokens are corrent, you should be getting something similar to the snapshot below. This indicates that we’ve successfully used OAuth credentials to gain authorization to query Twitter’s API.
Exploring Trending Topics
Now that we have a working Twitter resource object, we can start issuing requests to Twitter. Here, we will be looking at the topics which are currently trending worldwide using some specific API calls. The API can also be parameterized to constrain the topics to more specific locales and regions. Each query uses a unique identifier which follows the Yahoo! GeoPlanet’s Where On Earth (WOE) ID system, which is an API itself that aims to provide a way to map a unique identifier to any named place on Earth. The following code segment retrieves trending topics in the world, the US and in India.
Once you print the responses, you will see a bunch of outputs which look like JSON data. To view the output in a pretty format, use the following commands and you will get the output as a pretty printed JSON shown in the snapshot below.
To view all the trending topics in a convenient way, we will be using list comprehensions to slice the data we need and print it using prettytable as shown below.
On printing the result, you will get a neatly tabulated list of current trends which keep changing with time.
Now, we will try to analyze and see if some of these trends are common. For that we use Python’s set data structure and compute intersections to get common trends as shown in the snapshot below.
Interestingly, some of the trending topics at this moment in the US are common with some of the trending topics in the world. The same holds good for US and India.
Mining for Tweets
In this section, we will be looking at ways to mine Twitter for retrieving tweets based on specific queries and extracting useful information from the query results. For this we will be using Twitter API’s GET search/tweets resource. Since the Google Nexus 6 phone was launched recently, I will be using that as my query string. You can use the following code segment to make a robust API request to Twitter to get a size-able number of tweets.
The code snippet above, makes repeated requests to the Twitter Search API. Search results contain a special search_metadata node that embeds a next_results field with a query string that provides the basis of making a subsequent query. If we weren’t using a library like twitter to make the HTTP requests for us, this preconstructed query string would just be appended to the Search API URL, and we’d update it with additional parameters for handling OAuth. However, since we are not making our HTTP requests directly, we must parse the query string into its constituent key/value pairs and provide them as keyword arguments to the search/tweets API endpoint. I have provided a snapshot below, showing how this dictionary of key/value pairs are constructed which are passed as kwargs to the Twitter.search.tweets(..) method.
Analyzing the structure of a Tweet
In this section we will see what are the main features of a tweet and what insights can be obtained from them. For this we will be taking a sample tweet from our list of tweets and examining it closely. To get a detailed overview of tweets, you can refer to this excellent resource created by Twitter. I have extracted a sample tweet into the variable sample_tweet for ease of use. sample_tweet.keys() returns the top-level fields for the tweet.
Typically, a tweet has some of the following data points which are of great interest.
The identifier of the tweet can be accessed through sample_tweet[‘id’]
The human-readable text of a tweet is available through sample_tweet[‘text’]
The entities in the text of a tweet are conveniently processed and available through sample_tweet[‘entities’]
The “interestingness” of a tweet is available through sample_tweet[‘favorite_count’] and sample_tweet[‘retweet_count’], which return the number of times it’s been bookmarked or retweeted, respectively
An important thing to note, is that, the retweet_count reflects the total number of times the original tweet has been retweeted and should reflect the same value in both the original tweet and all subsequent retweets. In other words, retweets aren’t retweeted
The user details can be accessed through sample_tweet[‘user’] which contains details like screen_name, friends_count, followers_count, name, location and so on
Some of the above datapoints are depicted in the snapshot below for the sample_tweet. Note, that the names have been changed to protect the identity of the entity that created the status.
Before we move on to the next section, my advice is that you should play around with the sample tweet and consult the documentation to clarify all your doubts. A good working knowledge of a tweet’s anatomy is critical to effectively mining Twitter data.
Extracting Tweet Entities
In this section, we will be filtering out the text statuses of tweets and different entities of tweets like hashtags. For this, we will be using list comprehensions which are faster than normal looping constructs and yield substantial perfomance gains. Use the following code snippet to extract the texts, screen names and hashtags from the tweets. I have also displayed the first five samples from each list just for clarity.
Once you print the table, you should be getting a table of the sample data which should look something like the table below but with different content ofcourse!
Frequency Analysis of Tweet and Tweet Entities
Once we have all the required data in relevant data structures, we will do some analysis on it. The most common analysis would be a frequency analysis where we find out the most common terms occurring in different entities of the tweets. For this we will be making use of the collection module. The following code snippet ranks the top ten most occurring tweet entities and prints them as a table.
The output I obtained is shown in the snapshot below. As you can see, there is a lot of noise in the tweets because of which several meaningless terms and symbols have crept into the top ten list. For this, we can use some pre-processing and data cleaning techniques.
Analyzing the Lexical Diversity of Tweets
A slightly more advanced measurement that involves calculating simple frequencies and can be applied to unstructured text is a metric called lexical diversity. Mathematically, lexical diversity can be defined as an expression of the number of unique tokens in the text divided by the total number of tokens in the text. Let us take an example to understand this better. Suppose you are listening to someone who repeatedly says “and stuff” to broadly generalize information as opposed to providing specific examples to reinforce points with more detail or clarity. Now, contrast that speaker to someone else who seldom uses the word “stuff” to generalize and instead reinforces points with concrete examples. The speaker who repeatedly says “and stuff” would have a lower lexical diversity than the speaker who uses a more diverse vocabulary.
The following code snippet, computes the lexical diversity for status texts, screen names, and hashtags for our data set. We also measure the average number of words per tweet.
The output which I obtained is depicted in the snapshot below.
Now, I am sure you must be thinking, what on earth do the above numbers indicate? We can analyze the above results as follows.
The lexical diversity of the words in the text of the tweets is around 0.097. This can be interpreted as, each status update carries around 9.7% unique information. The reason for this is because, most of the tweets would contain terms like Android, Nexus 6, Google
The lexical diversity of the screen names, however, is even higher, with a value of 0.59 or 59%, which means that about 29 out of 49 screen names mentioned are unique. This is obviously higher because in the data set, different people will be posting about Nexus 6
The lexical diversity of the hashtags is extremely low at a value of around 0.029 or 2.9%, implying that very few values other than the #Nexus6hashtag appear multiple times in the results. This is relevant because tweets about Nexus 6 should contain this hashtag
The average number of words per tweet is around 18 words
This gives us some interesting insights like people mostly talk about Nexus 6 when queried for that search keyword. Also, if we look at the top hashtags, we see that Nexus 5 co-occurs a lot with Nexus 6. This might be an indication that people are comparing these phones when they are tweeting.
Examining Patterns in Retweets
In this section, we will analyze our data to determine if there were any particular tweets that were highly retweeted. The approach we’ll take to find the most popular retweets, is to simply iterate over each status update and store out the retweet count, the originator of the retweet, and status text of the retweet, if the status update is a retweet. We will be using a list comprehension and sort by the retweet count to display the top few results in the following code snippet.
The output I obtained is depicted in the following snapshot.
From the results, we see that the top most retweet is from the official googlenexus channel on Twitter and the tweet speaks about the phone being used non-stop for 6 hours on only a 15 minute charge. Thus, you can see that this has definitely been received positively by the users based on its retweet count. You can detect similar interesting patterns in retweets based on the topics of your choice.
Visualizing Frequency Data
In this section, we will be creating some interesting visualizations from our data set. For plotting we will be using matplotlib, a popular Python plotting library which comes inbuilt with IPython. If you don’t have matplotlib loaded by default use the command import matplotlib.pyplot as plt in your code.
Visualizing word frequencies
In our first plot, we will be displayings the results from the words variable which contains different words from the tweet status texts. Using Counter from the collections package, we generate a sorted list of tuples, where each tuple is a (word, frequency) pair. The x-axis value will correspond to the index of the tuple, and the y-axis will correspond to the frequency for the word in that tuple. We transform both axes into a logarithmic scale because of the vast number of data points.
Visualizing words, screen names, and hashtags
A line chart of frequency values is decent enough. But what if we want to find out the number of words having a frequency between 1–5, 5–10, 10–15… and so on. For this purpose we will be using a histogram to depict the frequencies. The following code snippet achieves the same.
What this essentially does is, it takes all the frequencies and groups them together and creates bins or ranges and plots the number of entities which fall in that bin or range. The plots I obtained are shown below.
From the above plots, we can observe that, all the three plots follow the “Pareto Principle” i.e, almost 80% of the words, screen names and hashtags have a frequency of only 20% in the whole data set and only 20% of the words, screen names and hashtags have a frequency of more than 80% in the data set. In short, if we consider hashtags, a lot of hashtags occur maybe only once or twice in the whole data set and very few hashtags like #Nexus6 occur in almost all the tweets in the data set leading to its high frequency value.
Visualizing retweets
In this visualization, we will be using a histogram to visualize retweet counts using the following code snippet.
The plot which I obtained is shown below.
Looking at the frequency counts, it is clear that very few retweets have a large count.
I hope you have seen by now, how powerful Twitter APIs are and using simple Python libraries and modules, it is really easy to generate very powerful and interesting insights. That’s all for now folks! I will be talking more about Twitter Mining in another post sometime in the future. A ton of thanks goes out to Matthew A. Russell and his excellent book Mining the Social Web, without which this post would never have been possible. Cover image credit goes to Social Media.
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